The paper investigates the effect that subnational networks of immigrants and emigrants had on exports from Spanish provinces (NUTS3) over the period of 2007–2016 by integrating state-of-the-art advances in the gravity model literature. In particular, it allows for heterogeneity in provincial export capacity, which significantly reduces pro-export effects, and select the Poisson Pseudo-Maximum Likelihood as the most suitable estimator according to diagnostic tests. When both immigration and emigration are instrumented, the pro-export effect of immigrants found by previous studies vanishes and that of emigrants, instead, appears appreciable. The results obtained suggest that over the period that encompasses the double-deep crisis, immigrants did not show significant information and enforcement effects in the considered context, while the effects of emigrant demand for home-country goods may have been important. The prevalence of emigrant over immigrant effects appears attributable to a change in the composition of the migration stocks over the considered period of crisis. The economic effects of migration have gained centrality in the academic debate, with major contributions addressing effects on employment, productivity, trade, and innovation.Before the sudden halt brought about by the outbreak of the COVID-19 pandemic in the early 2020, migration flows towards OECD countries and across the world witnessed a persistent upsurge . Due to the localized nature of knowledge spillovers  strategy, we do not find robust evidence of an immigration effect on exports. Exports from Spanish provinces appear mainly driven by emigrants, and the ability of immigrants to promote trade appears to deteriorate with the global financial downturn.The remainder of this paper is organized as follows. In Sect. immigrants on trade in their recipient countries. Furthermore, when implemented at the subnational level, analyses have generally assumed that the trade capacities of the recipient units of investigation  are homogeneous. Both choices entail problematic implications that we address by integrating the role of emigrants along with that of immigrants and by allowing for export capacities to be heterogeneous across subnational units of analysis.The analysis of the trade impacts of migration has so far mainly concentrated on the effects of information effect). Second, within their transnational networks, migrants can put in place implicit enforcing mechanisms  for international contractual relationships and compensate for the weakness of institutional protection mechanisms (enforcement effect). A different kind of effect (preference effect) refers to migrants’ preferences for products from their homeland, which increases trade unidirectionally, with emigrants adding to the foreign demand for exports and immigrants increasing the domestic demand for imports  rather than the NUTS2 level .In an attempt to fill this gap, in the empirical application that we propose with panel data, we allow for subnationally heterogeneous export capacities at the NUTS3 level (We investigate the role of migration in driving the export performance of 50 Spanish provinces (NUTS3) towards 109 destination countries over the 10 years of 2007–2016. Compared to the previous study of the migration–trade link in Spanish provinces by Peri and Requena-Silvente , we inclGiven the particular trends that migration and exports revealed over this crisis period, studying their relationship represents an interesting exercise to evaluate the endurance of migration effects along the business cycle. To the best of our knowledge, this is the first study to perform such an analysis.The dataset used for the empirical analysis is a balanced panel. Export data are retrieved from the official statistics of the Ministerio de Economia, Industria y Competitividad (MEIC) in Spain. For the sake of illustration, Fig. The specification that we employ to estimate the gravity model in Eq. mentclasspt{minimaOn the basis of the previous choices, our identification strategy ultimately draws on two main sources of variation: the cross-sectional variation between provinces in the same region–country pair and the time variation within the region–country pair, which is not explained by country- and province-specific shocks. We are confident that this approach allows us to control for most confounding factors that would pose a threat to the internal validity: most importantly, that the more economically dynamic provinces within a given region are simultaneously the strongest exporters and the strongest attractors of migrants. At the same time, our identification strategy allows us to exploit the time and cross-sectional variation in the data, which is relevant for understanding the phenomenon.j living in province i at time ti living in country j at time t (j to (from) province i in year t is equal to zero, and zero otherwise. Thus, our benchmark econometric model is the following:We augment the resulting three-way fixed-effects gravity model by adding our variables of interest: the stock of immigrants from country As the extant literature has highlighted, the choice of the most suitable estimator for Eq. A non-negligible share of the bilateral export values in our sample—about 7.5%—are zeros. The estimates of a standard OLS log-linear specification would only be based on positive trade values, posing a problem of selection bias. Previous studies have addressed the issue by opting for a Tobit model, with an arbitrary zero or an estimated threshold  estimator, which naturally accommodates zero trade flows, was recommended by Santos-Silva and Tenreyro  as a “woPPML, and other estimators with the dependent variable in levels, is also recommended when the error term is heteroskedastic  affect the trade of provinces differently depending on the initial share of immigrants from country j.We address this issue with an instrumental variables approach. To instrument immigration stocks, we resort to a slight modification of the standard shift-share instrument drawn from the labor economics and economic geography literature  from 1988The availability of data on residential variation  expressed by emigrants could reasonably be the greatest, if not the only, relevant channel through which migrants can affect trade. Moreover, due to the wider set of mechanisms underlying their pro-export impact, the effect of emigrants may be easier to detect statistically. On more structural grounds, the same results can also be related to the level of productivity of the firms based in Spanish provinces. Previous evidence reveals that emigrants are more effective in promoting the trade of low-productivity firms, which have a lower capacity to enter into foreign markets (following Melitz’s selection argument) but a greater chance of overcoming trade barriers, with the help of emigrants, once they have entered Hiller .Before taking these results as conclusive, three further steps are required to ensure that our estimates are reliable: i) ensuring that the estimates are robust to the bias-corrected method proposed by Weidner and Zylkin ; ii) addWeidner and Zylkin  showed tTable 22Columns (1) and (2) report the first-stage regressions. The F-statistics of both first-stage regressions, comfortably above the conventional value of 5, allow us to dismiss concerns about the potential weakness of our baseline instruments. As previously mentioned, this is a common result for instrumental variables such as our Altonij–Card-like instrument for immigration (column 1). Importantly, the F-statistics reported in column 2 are also reassuring in terms of the strength of our less-standard instrument for emigration.Column (3) reports the second-stage estimates of the just-identified model, where the orthogonality of the instruments must be assumed. Results confirm the results of the Poisson PML and highlight an even larger role for emigrants than the one we identified in the baseline estimates, similarly to Bratti et al. , 2020.23p-value = 0.7927) and prevents us from rejecting the null hypothesis of instrument orthogonality.In column (4), we report the results of the overidentified model that includes the additional instrument based on the 1988–1998 flows. Again, the F-tests on the first stage strongly reject the null hypotheses of weak instruments. The estimated 2SLS coefficients of immigration and emigration are remarkably similar to the ones in column (4). As for the overidentification test, the Hansen J statistic As the extant literature has largely shown  estimators with multi-way fixed effects. Through our diagnostic tests, implemented for the first time to the analysis of the migration–trade link and with panel data, we compared OLS, GPML, and PPML estimators. The Poisson PML (PPML) estimator emerged as the most suitable in the analysis of the pro-export effect of migrants for Spanish provinces. The magnitudes of the PPML, the Gamma PML (GPML), and the OLS estimates are comparable to each other, suggesting that the model is well specified and not substantially affected by the zeroes in the dependent variable. On the other hand, the OLS estimator was discarded on the grounds of heteroskedasticity, with important implications for the obtained results.By exclusively relying on the OLS estimates, we would have concluded that immigrants exert a significant pro-trade effect along with emigrants, consistent with previous studies. Instead, neither of our PML models supports such an inference in the considered timeframe. Rather, our estimates indicate and robustly confirm a positive effect of emigrants only on the exports of Spanish provinces. This result is robust to an instrumental variables approach and to the bias correction proposed by Weidner and Zylkin .The magnitude of the effect identified implies that a 10% increase in the emigrant population from a given province to a certain country would increase its exports towards it by almost 1%. Disregarding the heterogeneous exporting capacity of provinces would have led to an undue overestimation of both immigrant and emigrant effects. When compared to the few previous studies that include both immigrants and emigrants in export effects, the stronger and more robust effect of emigrants represents an interesting result. These results could be interpreted in light of the peculiar phase of the business cycle and of the impact of the global financial downturn. These have apparently eroded immigrants’ abilities to promote trade. Omitting the outward side of migration would have made this distinction impossible and may have wrongly attributed to immigrants a role that is actually played by emigrants.Importantly, the insignificant immigrant effects bear implications about the mechanism underlying the pro-trade effects. Indeed, the emigrant effect could be either an information, an enforcement, or a preference effect. Considering that no robust evidence could be found in support of immigrant effects, which are exclusively driven by the first two mechanisms, one may argue that the effects that we identify are due to a preference effect only. While this may be the primary mechanism driving emigrant effects, the results of our heterogeneity analysis still support immigrant effects compatible with the information effect: that is, immigration effects were found in high-concentration provinces and in provinces less specialized in intermediate goods. Accordingly, we are inclined to attribute the declining role of immigration to a deterioration in the trade opportunities that are amenable to the mediation of immigrants, as well as to a change in immigrant composition. Further research may seek to confirm this interpretation during more positive phases of the cycle and, more generally, may further investigate the relationship between the business cycle and the migration–trade link.Original results also emerge when the new methodological setting that we have proposed is applied to investigate the nuances of the trade–migration link. Consistent with previous literature, the effect of emigration is stronger in the trade linkages that provinces establish with more institutionally distant countries, i.e., with non-EU countries in our setting. Institutional distance actually represents a transaction cost, which the business networks created by emigrants can contribute to alleviating. An opposite result emerges with respect to language commonality, as Spanish emigrants to Spanish- and English-speaking countries have a magnifying, rather than reduced, pro-export effect. This result suggests that language skills also directly affect the individual ability of migrants to promote exports. Hence, language commonality could be a leverage for better detecting and exploiting trade opportunities. In other words, among the potential trade opportunities that a migrant can facilitate, some could be lost due to language differences. This would have the effect of reducing the emigrants’ capacity to promote trade (cfr. the random encounter model in Wagner et al. Additional insights from our application concern the novel contribution regarding the effect of networks of expatriates on exports via local and nation-wide networks. Results show that both matter. However, the existence of a network of Spanish expatriates in the same country is more important than their provinces of origin, with a very large elasticity. This likely indicates that an increase in emigrant stocks triggers a demand effect that not only promotes trade from the province of origin but also from Spain as a whole. The intertwining of local and non-local effects of emigration on trade also represents a newly emergent piece of evidence on which future research should concentrate.


Understanding the behavior of consumers and especially the purchase-related behavior has been a focus of research for the past decades. Thus, researchers and practitioners are curious to know how purchase patterns are different under different conditions such as product category, price, feeling, and so on. The primary focus of this study was to examine how the price of the products influences the purchase behavior of consumers across hedonic and utilitarian categories under regulatory focus theory (RFT). The secondary insight was to examine how mood can moderate this impact. We conducted three experimental studies to examine these research questions regarding the preference of consumers of hedonic (utilitarian) products when the price is low (high) and at different mood conditions in this purchase process. The results confirmed our hypothesis that product category has a significant impact on purchase choice of products and mood can mediate this impact. In the last section, we discussed the theoretical contribution, strategic insights for product designers and marketers, and possible future research directions. Academia researchers and practical business managers are always interested in what can affect consumer purchase choices. In this study, we examined consumer purchase preference between two types of products: one is mainly in hedonic nature and the other is mainly in utilitarian dimension. We compared preferences for these products under two price conditions: laptops (high-price conditions) and cell phones (low-price conditions). Based on the regulatory goals and loss aversion theory literature , we propThe remainder of the study is organized as follows. In the section “Theory and Hypotheses,” we defined the terms “hedonic” and “utilitarian” and then briefly reviewed the prior research relevant to regulatory focus, loss aversion, and mood effect in decision-making. Hypotheses are formed based on the review of relevant literature. In the section “Materials and Methods,” we conducted three experimental studies to confirm the hypothesis that we proposed. Then we concluded this study with a discussion of the theoretical contribution, managerial implications, and future research directions.Hedonic and utilitarian aspects of products play an important role in consumer choice . HedonicAt the attribute-specific level, we can also define each product attribute as hedonic and utilitarian . WhetherAmong these studies, Consumers usually purchase products based on whether the performance of products can meet their goals. People tend to approach pleasure and try to avoid pain . The undvice versa for the prevention-focused consumers. Specifically, consumers expect to fulfill the promotion goals on the hedonic attributes, whereas they expect to fulfill the prevention goals on the utilitarian attributes.Fulfillment of the promotion goals by product consumption leads to the pleasure experience. Promotion-focused consumers are more likely to obtain achieving pleasure; thus, they pay more attention to hedonic attributes that can generate pleasure. Fulfillment of the prevention goals by product consumption tries to eliminate the painful experience. Prevention-focused consumers focus more on avoiding undesired outcomes; thus, they pay more attention to utilitarian attributes . For exaWhen consumers are making choices, there is always a risk accompanied by the choice. People tend to underestimate outcomes that are merely probable compared with the outcomes that will happen for sure . In addiBased on the literature review of hedonic and utilitarian concepts, regulatory focus theory (RFT), and loss aversion theory, we developed the first part of hypotheses about choices between hedonic and utilitarian products under different price conditions. Specifically, consumers expect to fulfill the promotion goals on the hedonic attributes to get pleasure during the consumption, whereas they expect the fulfillment of prevention goals on the utilitarian attributes that offer security and safety of consumption. Besides, people are more afraid of loss than being happy with gain. The price of the product largely impacts the financial risks that consumers are facing. The potential loss of hedonic product consumption is even magnified when the price is high. In this situation, prevention focus will take lead and direct to the selection of utilitarian goods for positive gains from functional benefits. The concern of potential loss is small or the loss can be bared even there is when the price is low, thus the promotion goal will take lead and direct to the selection of hedonic goods.H1: When the price of the products is high, the concern of loss is also higher, consumers tend to avoid potential loss by meeting the prevention goals of choosing utilitarian products.H2: When the price of the products is low, the concern of loss is also lower, people care more about the gaining of pleasure by meeting the promotion goals of choosing hedonic products.H3: The choice preference can be attenuated by manipulating the promotion and prevention goals of consumers.Research has made considerable progress in understanding affect, showing that moods, feelings, and emotions are related to nearly all aspects of consumption behavior. People regulate their affective states to achieve hedonic gratification or to achieve instrumental goals. Both forms of goals pursuit are grounded in the classic approach-avoidance paradigm: from the hedonic perspective, people try to maintain positive mood states and avoid negative mood states, while from the instrumental perspective, people seek to achieve positive goals and avoid negative outcomes . The hedThus, mood can affect the choice through the two different regulatory focuses. When consumers are in a negative mood, they are motivated to improve that mood  has an effect on consumer choice between hedonic and utilitarian products, and this effect can be attenuated by the manipulation of the goals and mood of consumers. Cell phones and laptop computers are selected since participants should be familiar with them as undergraduate students from a Midwest business school (Lubar Business School at UW-Milwaukee in the United States), and they could also imagine the products in various usage scenarios.The purpose of this study was to testify the impact of the product price on consumer purchase choices between hedonic and utilitarian products. A 2 (product benefits: hedonic vs. utilitarian) by 2 (price: high vs. low) between-subjects design was used. A total of 120 undergraduate students participated in the study for extra credit. The between-subjects experimental design consisted of a high-price condition (laptop computers) and a low-price condition (cell phones). Participants were randomly assigned to one of the two experimental conditions (high vs. low price).First, we provided a booklet called “consumer choice-making questionnaire” to the participants. In the beginning, it stated “In this questionnaire, I am interested in your choice between hedonic and utilitarian products and your thinking processing of making the choice. In the following pages, you will read about the attributes of a chosen product. Please read the information carefully and answer the questions that follow.”Participants then read information about the two cell phones and two laptop computers. Each cell phone (laptop computer) was described as a combination of three hedonic attributes and three utilitarian attributes using the HED/UTI scale developed by After the participants finished the choice tasks, they were asked to list all the thoughts that came to their minds during the choice tasks. No time limit was set for this task. Finally, they were asked to fill out a questionnaire on demographics: gender, age, income, education, and household size.We conducted a pretest of the cell phone attributes to construct more realistic stimuli. We prepared a comprehensive list of approximately 50 attributes from real product manuals of the most popular cell phones in the market . We identified the top three influential attributes that offer hedonic benefits and the three most influential attributes that offer utilitarian benefits to construct the stimuli for the experiment. The same 120 participants were asked to rate all 50 attributes based on their importance. We first selected the 15 most influential attributes, and then picked 3 hedonic attributes and 3 utilitarian attributes. We created two levels of products (hedonic and utilitarian) by manipulating each attribute. One of the cell phone alternatives was high in style and attractiveness (hedonic) with a medium functionality (utilitarian). The other alternative was high in functionality (utilitarian) with a medium level of style and attractiveness (hedonic).A similar pretest of laptop computers was conducted. We created a comprehensive list of 25 attributes from real product manuals of the most popular laptop computers in the market . We first selected 10 topmost influential attributes on purchase decisions, and then picked 3 hedonic attributes and 3 utilitarian attributes. We created two levels of products (hedonic and utilitarian) by manipulating each attribute. One of the laptop computer alternatives was high in style and attractiveness (hedonic) with a medium functionality (utilitarian). The other alternative was high in functionality (utilitarian) with a medium style and attractiveness (hedonic).The hedonic cellphone attributes included oyster flip, changing phone colors, and programming new ring tones (yes/no). The utilitarian cellphone attributes included the level of network coverage (95% vs. 98%), battery capacity (2 days vs. 3 days), and sound clarity (medium vs. high).The hedonic laptop attributes included screen size (regular vs. widescreen), changing laptop color, and flipping the screen. The utilitarian laptop attributes included the level of processing speed (1.5 GHz vs. 3 GHz), memory size (2 GB vs. 4 GB), and battery life (4 h vs. 6 h).p-value was 0.0005, all indicating a significant difference at the two price conditions. The results of this task are consistent with our predictions in H1 and H2. In low-price conditions, the loss risk is low; people are more likely to fulfill the promotion goals because they care more about gaining pleasure. While in high-price conditions, the loss risk is high; people are more afraid of losing than gaining, so they prefer to fulfill the prevention goal to be safe. Also, as we can see, the magnitude of difference for choices between hedonic and utilitarian products is bigger in the high-price condition than in the low-price condition. This is probably because of the loss aversion theory that loss looms larger than gain  by 2 (price: high vs. low) by 2 (priming condition: promotion vs. prevention) between-subjects design was used. The same 120 undergraduate students participated for extra credit.Following the procedure used in prior studies  by 2 (price: high vs. low) by 2 (mood condition: good vs. bad) between-subjects design was used. The same 120 undergraduate students participated for extra credit.A “life events survey” served as the mood induction. This exact mood induction has seen application in numerous behavioral studies and is effective in eliciting the desired moods in subjects . ParticiThen, we assigned participants in two mood conditions to both the high-price choice task and the low-price choice task. Participants were given the same choice tasks as used in the first study. The design and the instructions were the same as in Study 1. We followed the same steps and processes to construct the exact same cell phones and laptop computers for Study 2.p-value was 0.0002, all indicating a significant difference at the two price conditions. At the good mood condition, the preference for the utilitarian item was higher than the results of Study 1 for both high- and low-price choice tasks. When choosing a cell phone, 46% of the respondents selected the utilitarian option. When choosing a laptop, 80% of respondents selected the utilitarian option. The chi-square statistic was 14.35 and the p-value was 0.0001, all indicating a significant difference at the two price conditions. Significant differences in two mood conditions with two price level products indicate a significant main effect of price and interaction effect of price and mood (p-value was statistically significant, indicating that the main effect of mood is also significant. The results support Hypothesis 4.A chi-square test was performed to examine the effects of price on the consumer choices between hedonic and utilitarian products under different mood conditions. We predicted that the preference of hedonic items in the low-price condition and the preference of utilitarian items in the high price condition will be attenuated by manipulating mood. The data showed a consistent effect of price as the results of study 1 just with a smaller magnitude in both conditions. Thus, in the bad mood condition, the preference for the hedonic item is higher than the results of Study 1 for both high- and low-price choice tasks. When choosing a cell phone, 73% of the respondents selected the hedonic option. When choosing a laptop, 40% of respondents selected the hedonic option. The chi-square statistic was 13.57 and the and mood . We compWe demonstrated the different consumer preferences of the hedonic and utilitarian products at high/low-price conditions, and this can be attenuated by mood. The summary of the results is shown in Building on the previous research , we connThe findings of our study could have significant implications for marketers in various cases such as product design, especially in the retailing industry. Product designers and marketing managers are often compelled to make selections among a variety of attributes due to the budget constrain and time limitation of productions. If there is no budget constrain, the best solution should be to maximize all the product benefits. However, manufacturers can only invest the money in the highest return attributes. Often, product designers should emphasize more on one attribute than another or even select one over another. In this situation, our study should be able to provide some insights about the product designs for the managers.For the retail stores, the findings of this study can also help them develop more effective promotions and store environments. Advertising has been a hot topic in consumer behavior research, and it has significant implications in the real market. We took a unique perspective of the role of mood in advertising effectiveness, from the regulatory focus aspect. When running the television spot for different products, marketers should consider selecting a cheer-up sadness ad for hedonic products and a maintaining happiness ad for utilitarian products. As for the store environment design, they can try to generate a more promotion goal-oriented environment for the hedonic products while generating a more prevention goal-oriented environment for the utilitarian products.First, the questionnaires in this study were answered by undergraduate students from a Midwest university, which may have its own limitations of a non-representative sample. Future research can use a more general population, which can represent the true market composition. Second, we used the same group of participants in all the three studies. In the future, different groups of participants can be used for external validity. Third, mediation analysis can be applied to test the direct and indirect effects of price on product choices and explore the underlying mechanism. Fourth, there are also so many other factors that might affect the consumer choices between hedonic and utilitarian products, such as gender, income, age, personal values, and innovativeness. Some might affect the choice through the regulatory focus while some others through different underlying thinking processes. We can either control these factors or explore their impact through further studies. Finally, we included laptops and cellphones as the product to be examined since the subject are familiar with them. As the participating subjects become different and being more representative, different products suiting the new conditions can be used. Also, different sets of products can be included for comparisons.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.Ethical review and approval were not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants was not required to participate in this study in accordance with the national legislation and the institutional requirements.SZ and PC contributed equally to this work, they contributed to the empirical work, to the analysis of the results. YZ and FW contributed to overall quality, and supervision the part of literature organization and empirical work. FL contributed to developing research hypotheses and revised the overall manuscript. SZ was in charge of the article writing, communicating with the reviewers and made multiple revisions for final publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


K-nearest neighbour (KNN), and support vector machine (SVM).Speech signal is enriched with plenty of features used for biometrical recognition and other applications like gender and emotional recognition. Channel conditions manifested by background noise and reverberation are the main challenges causing feature shifts in the test and training data. In this paper, a hybrid speaker identification model for consistent speech features and high recognition accuracy is made. Features using Mel frequency spectrum coefficients (MFCC) have been improved by incorporating a pitch frequency coefficient from speech time domain analysis. In order to enhance noise immunity, we proposed a single hidden layer feed-forward neural network (FFNN) tuned by an optimized particle swarm optimization (OPSO) algorithm. The proposed model is tested using 10-fold cross-validation over different levels of Adaptive White Gaussian Noise (AWGN) (0-50 dB). A recognition accuracy of 97.83% was obtained from the proposed model in clean voice environments. However, a noisy channel is realized with lesser impact on the proposed model as compared with other baseline classifiers such as plain-FFNN, random forest (RF),  K-nearest neighbours (KNN), are presented in classification algorithms to predicates ; in case the index is available by default along with the data set, index verification is to be started for matching the index with the voice clips in the database, as, in many cases, the index may lose some voice clips and that will create an error on the further process. Preprocessing of a voice signal refers to all the changes that apply to the signal before it is actually passed to the analyser. However, preprocessing is proceeded by sampling where the signal is converted to a set of samples for efficient analysis. Herein, as the speaker recognition system might deal with a large number of speakers, the data set preparation is an important step in preprocessing . HereinaS[n], let the S′[n] = S(n − 1) be the time-shifted copy of the same signal. Cross-correlation (C[n]) can be given inFundamental frequency is one of the interesting features in speech signal; it can be produced in time domain analysis using the cross-correlation approach. The aim of this feature is to identify the fundamental frequency in the speech signal . The funFigures T[n] and can be presented by the following equation  as given inS[n] is the fast Fourier transform of the sampled signal s[n]; furthermore, Mel frequency is derived from the above components using the Mel conversion equation as in (where on as in . (4)fm=2fm is frequency of speech signal in the Mel scale and fn is the Hertz scale speech frequency.where The last step in the Mel frequency spectrum algorithm is to simulate the human ear perception to the voice signal. Therefore, the filter bank in used to perform the same. The filter bank with the transfer functions given below is implemented to produce the human ear voice perception. The filter bank response to the input can be demonstrated in Features of speech signal are generated from both the Mel frequency spectrum coefficient (MFCC) method and the fundamental frequency method (pitch frequency). The Mel scale of the speech signal is obtained from the MFCC method which represents the human ear response to speech, and it basically divides the speech signal into a set of windows using a triangular filter bank and returns different window sizes for the higher and lower frequencies. In other words, MFCC may segregate the speech signal according to the frequency range presented on it and depends on the Mel scale (human ear perception). Furthermore, the pitch frequency is also obtained from the speech signal; it produces a single value in hertz; the fundamental frequency is vital in speaker recognition because it presents the minimum frequency of vocal chord vibration , 17. Thee-29. Three experiments are made as the time line of enhancement to the model; in each experiment, the model is upgraded for the sake of performance enhancement. Therefore, the first experiment relied on the parameters given in The feed-forward neural network model is used in this project to predict the speaker characters. However, the features of each speaker are applied to the model for training; model is established according to the parameters in A second experiment was done according to the results monitored from the first experience; the performance of the neural network is realized for all 100 repetitions; hence, the weight of every repetition is recorded. However, weight freezing technology involves presetting of the weight values of the FFNN model to those weight values that return the best cost. The technology of freezing dispenses with the need for a training algorithm as ready-made weights can be fed into the model with predetermined performance. The selection of proper weight values depends totally on the previous experiment which involves the record of weights and their cost values. The third experiment is made as another attempt to enhance the performance of prediction, so the new algorithm is used as a training algorithm. Particle swarm optimization algorithm is proven to have noticeable performance in optimizing the feed-forward neural network. As discussed in the previous sections, the feed-forward neural network is examined under several performance metrics in order to identify the best model that is capable of predicting the speaker identity. Three models are used, namely, the plain feed-forward neural network, the weight freezing-based feed-forward neural network, and ultimately the particle swarm optimization-based feed-forward neural network. Results of those models' performances are listed in K-nearest neighbour (KNN), and support vector machine (SVM). In order to evaluate the performance of all proposed tools, k-means validation is used where various input styles were tested. The accuracy scores of our proposed model as well as the other algorithms are demonstrated 10-folds in various noise conditions as in Figures 12Furthermore, different classifiers were used for predicting the speaker identity such as the random forest (RF) algorithm, The proposed classifier e.g. PSO-FNN had yielded best accuracy score during all noise conditions as demonstrated in Speaker recognition is a vital stage in many personal authentication and security systems; it builds the logic of person verification using their biometrical features, more specifically, voice features. The entity of the speaker recognition system involves the major two stages called feature extraction and speaker classification. However, these processes may begin with voice preprocessing involving the preparation of voice signals and set them together in the data set. Speech features include time domain and frequency domain processing; each is an integral part of speech processing and can be used to form a final recognition system. Speech signal preprocessing is about signal enhancement by reducing the noise level and removing other unnecessary information such as background noise and other associates. It might involve silence removal, which deletes the samples of low power that represent the silence in the uttered sentence (breaks while speaking). These processes are important to enhance the signal quality, which makes the signal more readable by the further process (stages). However, preprocessing is important to reduce the extra computation power that might utilize the capacity of the processor and distort the performance of the entire system. On the other hand, several approaches are knocked out to perform feature extraction of the speech signal. The fundamental frequency and Mel frequency cepstrum coefficients are the main approaches employed over this system, whether deep learning approaches are however employed for speaker classification tasks (mapping the features to particular speakers). FFNN is used for mapping the features to their perspective speaker, and the results have shown that PSO-FFNN outperformed the other techniques used in this paper.


Increasing the intensity to which high power laser pulses are focused has opened up new research possibilities, including promising new approaches to particle acceleration and phenomena such as high field quantum electrodynamics. Whilst the intensity achievable with a laser pulse of a given power can be increased via tighter focusing, the focal spot profile also plays an important role in the interaction physics. Here we show that the spatial-intensity distribution, and specifically the ratio of the intensity in the peak of the laser focal spot to the halo surrounding it, is important in the interaction of ultraintense laser pulses with solid targets. By comparing proton acceleration measurements from foil targets irradiated with by a near-diffraction-limited wavelength scale focal spot and larger F-number focusing, we find that this spatial-intensity contrast parameter strongly influences laser energy coupling to fast electrons. We find that for multi-petawatt pulses, spatial-intensity contrast is potentially as important as temporal-intensity contrast. Laser intensity is a key parameter in laser–solid interactions as it strongly influences the motion of electrons in the laser field and the temperature to which plasma electrons are heated, and by extension, defines many of the properties of beams of laser-generated ions7, X-rays9 and high harmonics11. Laser intensity can be enhanced by increasing the pulse energy, decreasing the pulse duration or focal spot size, or by a combination of these approaches. Due to the damage threshold of optical materials and coatings, increasing the energy requires that the size of the beam, and thus the size of expensive optical components, also increases. In recent years, efforts to boost the intensity have therefore focused on generating shorter pulses or achieving tighter focusing. The latter, due to the costs involved in producing large diameter short focal length optics and potential damage from target debris, has led to innovative approaches, such as the use of disposable focusing plasma mirrors (FPM)14. These efforts and the employment of extremely tight focusing optics drive the need to understand how the resulting decrease in focal spot size influences the laser–plasma interaction physics and properties of the secondary sources of energetic particles and radiation.Since the first demonstration of lasing, there has been a continual drive to increase the achievable peak intensity of laser light, and in the case of solid state lasers this has resulted in an average increase of two to three orders of magnitude per decade. Intensities in the range 17. As a result, it also affects the properties of the beams of particles and radiation generated by those electrons. In the case of beams of ions accelerated by the target normal sheath acceleration (TNSA) mechanism7, it has been demonstrated to change the energy spectrum, spatial distribution and laser-to-ion energy conversion efficiency23.Previous studies on the role of laser focal spot diameter show that it, in part, defines properties of the fast electron population generated at the focus and accelerated into the targetwings surrounding the main spot). Relativistic electron motion occurs when the normalised laser amplitude c the speed of light, e is the electron charge, and F-number optics used to achieve tight focusing are more susceptible to laser wavefront aberrations and alignment sensitivity, which typically results in reduced focal spot quality and increased energy in the wings. The importance of considering the energy in the wings, in the accurate calculation of the main focal spot intensity, has been considered, for example in Hartmann et al.24, but the influence of this on laser-matter interaction physics has, to our knowledge, not been explored to date.As multi-petawatt laser facilities become operational and the peak power of laser pulses continues to increase, not only is the influence of the main laser focal spot size important, but so too is the intensity of the light in the halo region at larger radii , one can consider spatial-intensity contrast to be the ratio of the peak intensity to the intensity of the light at a secondary spatial location or defined area in the wings. Figure mentclass2pt{minimHere we report on an experimental and numerical investigation examining the influence of focal spot spatial-intensity contrast on laser–solid interactions. A tight focusing geometry is used and the properties of the beam of protons accelerated by the TNSA mechanism are measured as a function of the laser intensity in the wings. We find a To determine the influence of spatial-intensity contrast, Fig. 25 report less effective proton acceleration, in a shorter pulse regime . Using the 1D plasma expansion model introduced in Mora28, with the fast electrons spectrum assumed to be described by the electron temperature scalings presented in Wilks et al.29 and Haines et al.30, power scaling exponents of Overall, entclass1pt{minima32). However, the proton beam obtained with F/1 focusing is clearly more divergent over the full energy range than that produced by F/3 focusing. The average reduction in beam divergence between the two is a factor of Clear differences are observed in the measured proton beam spatial divergence as a function of proton energy for the two focusing cases, as shown in the representative measurements presented in Fig. 28 plasma expansion formulae (with the acceleration time truncated at 33). Using measurements of the encircled energy within the focal spot FWHM and the wing diameter for the F/1 FPM  without the presence of wings, the number of high energy electrons is observed to decrease, whilst the lower energy electron population increases due to the increase in the area of the target irradiated. This can be seen in the example case of the ratio of the electron spectra of a Figure Both the number and temperature of the fast electrons play an important role in defining the sheath field at the rear of the target. The increased coupling of energy to the electrons due to the presence of the wings results in an increase in the size of the sheath at the rear of the target, as shown in Fig. X is the particle position in the transverse direction. The efficiency values are normalised to the total energy for F/3 experimental condition at The increase in the transverse extent of the sheath field means that more protons are accelerated, and this is shown in Fig. Overall, and consistent with the experiment trends, we find that the total energy of the accelerated protons is strongly determined by the effective area and spatial energy distribution of the laser spot, although these parameters have much less influence on the maximum proton energies achieved.We next introduce a simple model to aid in determining the conditions for which the light level in the wings generates relativistic electrons and contribute significantly to the laser–plasma interaction physics. This will be the case when the normalised vector potential (defined above) for the light in the wings is F/3 and F/1 experimental conditions for which F/3 focal spot in the experiment. In Fig. F/1 FPM wing characterisation , it strongly defines the overall energy coupling to electrons and protons, as well as the divergence properties of the proton beam.As the efficiency of laser energy coupling to electrons and the properties of the resultant beam of fast electrons injected into the target are key factors in most laser–solid interaction topics, this study highlights the importance of spatial-intensity contrast as the field moves towards higher peak intensities achievable at multi-petawatt laser facilities. It is imperative that this parameter, which has received scant attention to date, is accurately characterised and considered in experiment design, modelling and the interpretation of measurements using these new facilities. It also merits consideration when analysing hitherto unexplained experimental results at the intensities achievable at present. Shot-to-shot variability in the pulse wavefront, which alters the ratio of the energy encircled in the focal spot to that in the wings, can significantly change the interaction physics when the intensity in the wings fluctuates around the relativistic threshold, and this should be considered for accurate description of experimental results. Just like pulse energy, duration, peak intensity and temporal-intensity contrast, focal spot spatial-intensity contrast should be considered an important parameter to measure in an experiment and to communicate when reporting results. Our example measurements of proton acceleration demonstrate that it is another parameter in defining the optimisation and reproducibility of laser-driven radiation from solids, and thus impacts on the development of these sources towards wide-ranging applications.p-polarised pulses of 1054 nm wavelength light, with energy up to 300 J on-target (after losses in the compressor and plasma mirror), and duration  configurations were deployed. The first uses an ellipsoidal focusing plasma mirror (FPM), depicted in Fig. F/3 off-axis parabola (OAP) focusing geometry used at the Vulcan Petawatt facility to F/1 focusing. Further details on the design and operation of this optic are provided in Refs.14. The second uses a planar plasma mirror (PPM) to maintain the F/3 OAP focusing after reflection. Typically the PPM forms a F/3 OAP focusing. The effect of non-optimised alignment on the FPM output focal spot is shown Fig. 14), that utilises a 532 nm light source, and thus smaller spots/wings are obtained when compared to FPM usage on the Vulcan laser (operating at 1054 nm). As the effect of unoptimised FPM alignment results in decreased focal spot intensity and increased spot wing size (thus irradiation area), this gives us a method to measure not only the effect of spot size at intensities similar to those achieved by the larger spot  and was focused to the front surface, as defined in the main text. The pulse duration is shorter than in the experiment (750 fs) due to restraints on the available computational resource. It was not possible to run the required number of simulations for the same duration as the pulses in the experiment. We have run test cases for a duration of 750 fs and find no significant difference when compared to the 400 fs-duration results. We also note that particle energies are exaggerated in 2D simulations (no energy dissipation out of the plane), resulting in an exaggerated degree of target expansion. A slightly shorter pulse in a 2D simulation thus more accurately models the expansion expected in the experiment. Finally, we note that test case simulations performed with 40 fs duration pulses (with and without wings) show changes in the overall energy coupling to fast electrons, but the conclusion on the role of the intensity in the wings remains unchanged. A more detailed investigation of the short pulse case will be the subject of a future study.


In recent years, speech recognition technology has become a more common notion. Speech quality and intelligibility are critical for the convenience and accuracy of information transmission in speech recognition. The speech processing systems used to converse or store speech are usually designed for an environment without any background noise. However, in a real-world atmosphere, background intervention in the form of background noise and channel noise drastically reduces the performance of speech recognition systems, resulting in imprecise information transfer and exhausting the listener. When communication systems’ input or output signals are affected by noise, speech enhancement techniques try to improve their performance. To ensure the correctness of the text produced from speech, it is necessary to reduce the external noises involved in the speech audio. Reducing the external noise in audio is difficult as the speech can be of single, continuous or spontaneous words. In automatic speech recognition, there are various typical speech enhancement algorithms available that have gained considerable attention. However, these enhancement algorithms work well in simple and continuous audio signals only. Thus, in this study, a hybridized speech recognition algorithm to enhance the speech recognition accuracy is proposed. Non-linear spectral subtraction, a well-known speech enhancement algorithm, is optimized with the Hidden Markov Model and tested with 6660 medical speech transcription audio files and 1440 Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) audio files. The performance of the proposed model is compared with those of various typical speech enhancement algorithms, such as iterative signal enhancement algorithm, subspace-based speech enhancement, and non-linear spectral subtraction. The proposed cascaded hybrid algorithm was found to achieve a minimum word error rate of 9.5% and 7.6% for medical speech and RAVDESS speech, respectively. The cascading of the speech enhancement and speech-to-text conversion architectures results in higher accuracy for enhanced speech recognition. The evaluation results confirm the incorporation of the proposed method with real-time automatic speech recognition medical applications where the complexity of terms involved is high. Speech-to-text transcription has gained importance in many applications and benefits in research, the military, medical sector, smart homes, transportation systems, automatic transcription on lectures, conversations, record-making . Speech The spectral subtraction algorithm extracts the speech from additive noise. Enhancement of speech was achieved by estimating the spectrum of the noise-free signal and subtracting the estimated noisy signal from an available observed signal. The spectral subtraction algorithm suffered from residual noise . The sigThe neural networks that learn the statistical information automatically using non-linear processing units were introduced for noise reduction. The deeper networks are considered to be more efficient in learning than the shallow networks . A deep Generative adversarial networks (GAN) were used to construct the clear speech signals from the noise signals over RNNs . MultiplGeneralized sidelobe canceller (GSC) has been used for non-stationary broadband signals. GSC separates the desired signals from the interference by exploiting spatial information about the source location . Noise sFrom the literature study, it is evident that Nonlinear Spectral Subtraction (NSS) and Iterative Signal Enhancement algorithm (ISE) are the most effective methods for speech enhancement . This paDynamic time warping (DTW) is a dynamic programming algorithm technique used for determining the correspondence between two sequences of speech that may differ in time or speed . For exaThis work aims to enhance the speech signal by suppressing the noise signals involved in voice control applications. Speech enhancement is an essential factor in speech recognition as it can be used as a pre-processor to enhance speech. Generally, the source of the noise signals can be background noise, electromagnetically induced noise. Reducing these noises will result in increasing the intelligibility of the speech and agreeable speech recognition. In this article, a hybridized speech enhancement algorithm is proposed with experimental results. Enhancement of speech is essential when the terms involved are complex and involve external noises. There are various speech enhancement (SE) algorithms available, which are discussed in the following section.The iterative signal enhancement algorithm is used for reducing the noise in speech. The algorithm is designed based on a trimmed singular value decomposition (SVD) procedure and can be used as a tool for enhancing the noisy speech signal by suppressing the additive noise present in the signal. Compared to the classical algorithms, the ISE algorithm escalates similar improvements in Signal-to-Noise Ratio (SNR).The ISE algorithm executes in two phases. The first phase produces an enhanced signal s[i] from the noisy signal ns[i]. The enhanced signal s[i], which comes from phase I, still contains some noise. So, the noise removal phase is repeated a certain number of times depending on the level of noise present in the signal. The signal decomposition of the enhanced signal at each level is given a rank ranging from 1 to level l. The first rank signal decomposition sd[i] for the s[i] is obtained by averaging the anti-diagonals of the Hankel matrix to rank-1. Hankel matrices are square matrices with constant ascending skew-diagonals from left to right. Hankel matrix is constructed from the signal is helpful for decomposition of constant signals and time-frequency representation. The rank-1 signal decomposition covers most of the energetic spectral band of the noisy input signal in the frequency domain.The signal decomposition sd[i] is subtracted from the input signal sn[i] to calculate the residual signal r[i], and the phase-I procedure is repeated by using the residual signal as the input for the upcoming iterations. The signal decomposition sd[i] is summed up throughout the iterations to obtain the enhanced signal s[i]. The iteration is stopped once the residual signal r[i] contains only the noise components. The phase-2 concentrates on the residual signal to maintain the number of iterations of phase-I. The working of the ISE algorithm is depicted in The ISE algorithm is advantageous over the typical enhancement algorithms as ISE has a better frequency selectivity for filtering out the noise than the standard algorithms.(1)Isolating the subspace as signal and noise subspaces from the original subspace(noise mixed speech)(2)Eliminating the noise-only subspace that has been isolated in step1.Subspace methods, also called high-resolution or super-resolution methods, use an eigenvalue analysis or eigendecomposition of the correlation matrix to derive frequency component values of a signal. The process for subspace-based speech enhancement can be stated as follows:s(i) represent the pure speech signals and let n(i) indicates the zero-mean additive noises mixed in the pure speech. The observed noisy speech x(i) can be given byAssume p × q) genuine autocorrelation matrices of x(i), s(i), and n(i), respectively, with q > p. Rx = Rs + Rn is obvious given the assumption of uncorrelated speech and noise. Regardless of the specific optimization criterion, speech enrichment is now obtained byBy nullifying the components in the noise subspace, the enhanced speech is constrained to inhabit only the signal subspace.Changing (decreasing) the eigenvalues of the signal subspace.Allow Rx, Rs, and Rn to be (n(i) with its corresponding eigenfilter vector (EVF) and its reversed eigenfilter vector (REFV). This filtered output is fed to the not gate (NG) and their summation results in the enhanced speech signal.The input speech signal with noise is split into unique spaces. Each and every space is individually processed. In y(i) denote the noisy speech, i.e., the pure speech signal s(i) is polluted with the noise signal n(i). Then in the time domain, their relationship is described as:Nonlinear Spectral subtraction is one of the most primitive and notably the most famous speech enhancement methods. NSS can be predominantly valid in cases where a boisterous environment contaminates an original speech signal with the same bandwidth as that of speech. To decrease external noise, the NSS algorithm is developed, which considers the change of signal-to-noise ratio across the speech spectrum and uses a distinct over-subtraction factor in each frequency band. To obtain their relation in the spectral domain, take the Discrete Fourier Transform (DFT) and power magnitude for Equation (1) with the assumption that noise and speech signals are uncorrelated, then the relation is described as follows:The spectral subtraction algorithm adapts the damaged speech signal’s short-term spectral magnitude appropriately. The signal is modified so that the synthesized signal feels as near to the unbroken voice signal as possible. A noise power estimate and a subtraction rule are used to calculate the appropriate weighting of spectral magnitudes.The Word Error Rate represents the amount of word error occurring during the speech. It can be calculated using the formulaThe primary purpose of speech to text (STT) or speech recognition is to enable a real-time dictation of audio signals into text. All the STT systems depend on the acoustic model and the language model. In the case of including the additional feature vocabulary systems, a pronunciation model can be used. It is challenging to construct a universal speech recognizer. To develop the best quality STT system, it needs to specialize in a specific language, idiom, application domain, speech type, and communication frequency.Hidden Markov Models (HMM) are mainly used for general-purpose speech recognition systems. In general, the speech signals are observed as a stationary signal whose amplitude and frequency remains constant. It takes a very short time scale for a speech to be estimated as a static process.In HMM, it is possible to train the data set automatically, which makes it easy computationally, and hence it is extensively used. The HMM would generate a sequence of n-dimensional real-valued vectors every ten milliseconds  in speech recognition. The first coefficient of a Fourier transform of a small part of the speech is extracted and decorrelated with the cosine transform to calculate the cepstral coefficient vectors.In each state of the HMM, a statistical distribution of a mixture of diagonal covariance Gaussians is performed to present the probability of each identified vector. In the current speech recognition systems, every word or phoneme has its output distribution, whereas, in HMM, the sequence of the terms or phonemes is constructed by cascading the individually trained cepstral vectors for the specific words and phonemes, respectively.Modern voice recognition systems use diverse combinations of various common strategies to improve outcomes beyond the fundamental approach. Context dependency of the phonemes is required for the traditional large-sized vocabulary system to enable the phonemes with different pre and post contexts to have unique HMM states. These HMM states can be used to normalize different recordings and the speaker conditions using the cepstral normalization method. Vocal tract length normalization (VLTN) can be used to further normalize male-female or other speaker criteria. To capture the speech dynamics, linking and linear discriminant analysis (LDA) based projects can be used, which is followed by either the heteroscedastic LDA step or global semi-tied covariance transform method. To optimize the classification-related measure of the training data, many systems employ discriminative training strategies, which can be used to avoid the purely statistical approach to estimate the HMM parameter.The HMM is used to convert speech features into HMM parameters and calculate all speech samples’ likelihood. Recognition of this likelihood of speech samples is used to recognize the spoken words. The speech signal can be a single, continuous, or spontaneous word. The spontaneous words are complicated as their speech is fast and the number of words is high. This spontaneousness creates complications in automatic speech recognition. The proposed work mainly concentrates on spontaneous speech in the medical domain. The words used in the medical environment are complicated, and the medication prescriptions need clarity. Hence, the idea is to develop a hybridized algorithm that can resolve the issue and assure accuracy.Algorithm 1 Hybrid Speech Enhancement AlgorithmInput y(i) the noisy speech signal with n(i) the noise and s(i) the clear speech signalSegmentation: the speech signal is segmented into frames.Process: The noisy signal is processed frame by frame and the Fourier transform is calculated asn y = s + where ‘r’ is the frame number.Calculation: The short-term power spectrum of y(i) is calculated as2 = |s(f)|2 + |n(f)|2 |y(f)|Estimation: By removing the noise from the input signal, the speech is estimated as follows:2 Obtain:2 is obtained by averaging the recent pause frames2 where N is the number of consecutive speech framesReconstruction:2 as the product of noise and the spectral subtraction factor2/|y(f)|2 Formulation: The Hankel matrix of the enhanced speech signal is formulated from step 6.Optimization: The enhanced speech is optimized using the least squares and the minimum variance optimal estimator astHs = ΣVConversion: The enhanced speech signal is converted to text.Output: The text format of the noise removed speech signal with reduced WER.The nonlinear spectral subtraction and the hidden Markov model are cascaded to form a hybrid architecture to enhance the accuracy of the speech recognition system. The NSS provides a better result for the spontaneous signal by suppressing its noise to a greater extent, and the HMM can give high accuracy in converting the speech to text. The proposed model has been tested on a dataset that contains medical speech of about 8.5 h and RAVDESS emotional speech dataset. The speech includes both male and female speeches, with each audio time ranging from 0.2 s to 60 s.Various speech enhancement algorithms are present to enhance the speech signal by reducing the noise in the surrounding. The proposed work tested the algorithms such as ISE, sunspace, and NSS for the medical audio dataset, and the findings are discussed in this section.One of the numerous issues that automatic speech recognition systems face is processing spontaneous speech. Spontaneous speech is characterized as utterances that comprise well-formed phrases similar to those found in written texts. Disfluencies  are the main characteristics of this type of speech, and numerous studies have concentrated on detecting and correcting them.The waveform and the spectrogram results produced by the ISE algorithm are shown in It is visible from The input noise contains the noise from the surroundings, such as the fan and vehicle noises. It is necessary to suppress the external noise to enhance the actual speech to improve its recognition irrespective of its length and complexity. Although the noise removal can be visibly seen in Spectral subtraction is one of the most basic and also possibly the most famous speech enhancement methods. The enhanced speech signal is converted into text using the HMM or the DTW-based text conversion algorithm to find the accuracy level of the enhanced speech signal. Comparing The WER calculated for the spontaneous signal after enhancing the signal using the NSS, subspace, and ISE are compared and shown in The accuracy and efficiency of the speech can be visualized using the time waveform, log transformation, and spectrogram. The text analysis is used to represent the efficiency of the speech with quantitative measurement so that the comparison of speech enhancement becomes accurate.The word error rate of a noisy signal and the enhanced speech signal is calculated using the HMM algorithm and is shown in There are various speech enhancement algorithms to reduce the domestic noises recorded during the speech and produce an enhanced signal. When the length of the speech (the number of words involved in the speech) increases, the performance of the algorithms does not seem to be effective as many of them work well with only small words. The enhancement also depends on the complexity of words involved in the speech. Specific applications such as medical speech need more accuracy as the terms involved are complex, and the noise removal should not affect the originality of the words uttered. Hence it is more important to select the best noise removal algorithm for complicated applications.In the proposed method HSEA, the speech enhancement algorithm NSS and the speech to text conversion algorithm HMM are cascaded to further increase the accuracy of the speech. In this study, an HSEA is employed for medical audio data to reduce the word error rate. The entire procedure of the proposed method consists of the NSS and the HMM architecture with the medical data set. The typical NSS algorithm is optimized with least squares and minimum variance and then cascaded with the speech-to-text algorithm HMM to establish the proposed HSEA. In HSEA, the speech is enhanced using the NSS algorithm, and again the enhanced speech is optimized using the optimization criteria of the subspace method. As the double layer of enhancement is performed, HSEA can perform competitively with other typical speech enhancement models such as NSS, ISE, and Subspace. The proposed model has been validated with 6660 medical and 1440 RAVDESS audio data. The validation of the proposed HSEA architecture has proven to achieve maximum accuracy of 90.5% with a minimum word error rate of 9.5% for medical speech transcription and accuracy of 92.4%, the word error rate of 7.6% for the RAVDESS audio data. The validation of the proposed HSEA architecture has proven to achieve maximum accuracy of 90.5% with a minimum word error rate of 9.5%.The proposed methodology can produce a clear speech signal as the output with reducing word error rate, making it efficient for all kinds of applications involving speech in open space. Despite the advantages, the methodology involves mathematical complexities as the layer of speech purification increases, which remains a limitation of the proposed method. Therefore, future work can concentrate on integrating the layers in such a way as to reduce the computational complexities and to achieve high efficiency.


Nanoelectromechanical systems (NEMS) have received great interest from researchers around the world since the advent of nanotechnology and nanoengineering. This can be attributed due to the unique characteristics of NEMS devices and their wide range of applications. Among these applications, nanobeams and nanotubes now have an important role in the design of a variety of NEMS engineering devices. In the current research, the thermoelastic vibration analysis of Euler–Bernoulli nanobeams has been investigated using the theory of non-local elasticity proposed by Eringen. Also to study the effect of temperature change, the generalized thermoelastic model with dual phase-lag (DPL) is applied. The studied nanobeam is subjected to an axial thermal excitation load and surrounded by a magnetic field of constant strength. The Laplace transform technique has been used to solve the system differential equations and to find an approximate analytical solution for the different physical fields of the nanobeam. The numerical results obtained for the studied variables have been graphically clarified and discussed analytically. The effects of various influencing factors such as magnetic field strength, temperature change, non-local parameter as well as ramp type parameter have been examined and studied in detail. The concept of relaxation time was incorporated into the standard Fourier law by Lord and Shulman as a simple idea2. They included the impact of heat flux rate in the flux-temperature relationship and demonstrated that, under such assumptions, the energy equation is a hyperbolic equation, resulting in thermal wave propagation at a limited speed.It is known that the energy equation in terms of temperature is a parabola according to the assumptions of the classical Fourier law. As a result, using the classic Fourier law, the temperature spreads at an infinite rate. To address this flaw, several ideas have been developed. Theories of generalized thermoelasticity, or thermoelasticity with the second acoustic effect, are the terms used to describe these ideas3 proposed the second generalized thermoelasticity theory, which included the temperature rate as a dependent term on two relaxation time factors. In the entropy expression and stress correlation, the authors identified two distinct lag periods. In compared to classical theory, the Green–Lindsay theory (GL) has more extensive energy and motion equations. Green and Naghdi in4 used the displacement-temperature-flux rate in Fourier's law to create a novel model that did not incorporate energy wastage. Tzou7, who provided two separate phase delays in the heat conduction Fourier's law, proposed a generalized thermoelasticity model with dual-phase lag (DPL). The first is for the heat flux vector, while the second is for the temperature gradient. In addition to the preceding models, a number of popular thermal elasticity models have been presented, all of which are based on the theories that have been generalised in a number of articles12.Green and Lindsay13. Nanobeams and nanotubes are now possible design possibilities in many engineering NEMS devices at the nanoscale scale14, and they are playing significant roles in many engineering NEMS devices. Due to their unique properties and functions, micro/nanoelectromechanical systems (MEMS and NEMS) have been of great research interest. These systems are widely used in many areas of technology and engineering, including vehicles and electronics15. Some micro and nano structures, as well as length and width differences that have a significant impact on the properties and performance of those structures, may have geometric irregularities during production, assembly and packaging. It is well known that the mechanical response of nanostructures is of large dimensions, thus small effects on the mechanical behavior of nanostructures must be considered16.Nanoelectromechanical systems (NEMS) have garnered a lot of interest from researchers all over the globe since the emergence of nanotechnology and nano-engineering. This is due to NEMS devices' unique characteristics and wide range of applications18, modified strain gradient19, couple stress20, modified couple stress21, and the theory of nonlocal continuum23. In nano-mechanical applications including distribution of elastic waves via grid, composite wave propagation, dislocating dynamics and mechanical broken waves, surface fluids, etc., theory of non-local elasticity was used. In all nanostructures, the mechanical conduct of the nano-tubes and nanobeams was studied most often. The fundamental difference between the conventional Eringen's nonlocal theory of elasticity is based on the definition of stress29.Classical continuum theories are well known because they are not able to correctly estimate the experimentally detected size-dependent behavior of small-scale structures; any sizes on the order of microns and sub-microns. To solve this problem, a few more general ordinary continuum mechanics theories have been developed and used to record size-dependent behavior involving additional physical constants and conventional Lamé constants. Some of these theories are the strain gradient23. For this reason, the state of stress at one location in the body is determined by the states of strain at all other sites in the body, according to the theory of non-local elasticity. This is in stark contrast to the traditional continuum mechanics theory, which states that the stress state at a given locus depends only on the strain state at the same locus. The theory of non-local elasticity has been widely used to explore nanomechanical applications such as nanostructure vibrations and wave propagation, fluid-loaded nano-device stability, dislocation mechanics, crack mechanics, etc.30.There is a growing interest among scientists in using Eringen's nonlocal elasticity differential model31 considered the self-adjointness of Eringen's nonlocal elasticity theory. It is demonstrated that Eringen's model may not be self-adjoint and that raising Eringen's small length scale coefficient can result in an unanticipated stiffening effect for a cantilever's fundamental vibration frequency. This is clearly inconsistent with all other boundary condition softening results, as well as the higher vibration modes of a cantilever beam. Eringen's nonlocal elasticity has recently been proven to be a good fit for microstructured (discrete) media31. Given that discrete media is a conservative system at the local level, a conservative macro-scale is logical . The softening phenomena generated by the small length scale effect is best described by Eringen's corrected nonlocal elasticity with self-adjoint boundary conditions, which cannot be attained with nonself-adjoint Eringen elasticity31. As a result, inside the micromechanics-based energy arguments, we recommend using differential equations with the specified associated boundary conditions. More details on this point can be referred to in36.Using simple one-dimensional beam models, ChallamelWe therefore recommend the use of the differential equations with the proposed associated boundary conditions within the micromechanics-based energy arguments.According to the classical nonlocal elasticity, the stresses at a given point of a continuum medium depend on the strains evaluated not only at the point itself but also in the overall volume. Clearly, this general integral constitutive relation leads to sophisticated integro-differential equations, which are challenging even for 1D problems. Therefore, Eringen proposed a simplified differential form, which may be considered as equivalent to general integral formulation problems, e.g. propagation of plane waves in an unbounded medium. Over the last fifteen years, this differential formulation has been often employed as a framework for problems instability and vibrations of nano- beams, plates, and shells, as well as carbon nanotubes.Among the excellent research mentioned above, only a few thermoelastic models were employed to study the thermal behavior of the nanobeam system, and they mostly focused on the linear frequency of free vibrations. According to past research and the author's expertise, the thermoelastic behavior of the nanobeam has not been investigated yet using generalized thermoelasticity models with phase lags. The thermoelastic vibration of nonlocal nanobeams subjected to a ramp-type thermal load and a longitudinal magnetic field is examined analytically for the first time when the nanobeam is simply supported in the current study.The thermoelastic vibration and mechanical behaviors of an Euler–Bernoulli nanobeam under thermal and magnetic environments are investigated in the following investigation due to the increasing use of nanostructures in nanotechnology and nano-devices Maxwell's equations are used to calculate the transverse Lorentz force due to the horizontal magnetic field vector. The analytical solutions of the normal deflections, temperature, and bending moments for Euler–Bernoulli nanobeams are obtained specifically with several unknown constants using the Laplace transformation. The thermoelastic responses of nanobeams are numerically investigated with the influences of a nonlocal parameter, a ramp-type parameter, and a magnetic field. For forced vibration responses of nanobeams, the variations between classical coupled theory and generalized thermoelasticity are also studied.As is evident from Eringen's non-local elasticity theory, stress at any point in the body depends on current stresses as well as strains elsewhere in the body. The dynamics and some experimental results of acoustic scattering are used in atomic network theory to explain Eringen's non-local elasticity. The integral space, which describes the weighted averages of the pressure tensor of a related body point, is one of the basic equations in this theory. The theory introduces the effect of small scale in the integral constitutive relationship of space.23For homogeneous and isotropic elastic solids, the non-local elasticity equation are22The local stress tensor In the above relation, 23:It reaches its peak at Also, when In addition, nonlocal theory should be able to approximate atomic lattice dynamics for small intrinsic characteristic lengths, i.e. when The following are some of the intriguing properties of the nonlocal modulus 37:When the kernel mentclass2pt{minim7 provides a simplified version of the classical thermoelastic model, in which the Fourier law is replaced by an approximation of the equationTzou in7 work to clarify the applicability of this type of theory.According to Eq. , the tem6. The following equation can be used to approximate the previous equation:We can make use of truncated Taylor expansions to replace Eq.  in Tzou'The heat flux Equation  describe6 is given byThe generalized DPL heat transfer equation proposed by Tzou32Due to the application of an initial magnetic field The Lorentz force Consider slight bending thin nanobeam with sizes mentclass2pt{minimThe displacements are therefore are given by22:The non-local constitutive Eq.  can be wThe equilibrium conditions of the Euler–Bernoulli beam can be described as the transverse nanoscale vibrations which can be described by the equationBending moment with the help of Eqs.  and 15)15) can aFrom Eqs.  and 16)16), we gThe force per length In addition, the flexure moment Inserting Eq.  into Eq.The DPL heat equation without the heat source The upper and lower sides of the beam have no heat flow , That's how it is Thus, Eqs.  and 21)21) becomMultiplying Eq.  by meansIn order to simplicity the basic equation, the following non-dimensional quantities are considered:The governing equations are therefore simplified as (dropping the primes)It is important to emphasize that both initial conditions and boundary conditions must be considered in order to solve the problem. Suppose that our problem is homogeneous in the initial conditions. First, the initial requirements are consideredThe following boundary conditions can be taken into account. We will assume that the nanobeam is simply supported at the two endsWe also consider, that the first end Furthermore, at the end side Applying the Laplace transform to Eqs. –30), th, th30), By elimination of from Eq. , we infewithEquation  can be eIntroducing into Eq. , one getThe roots of Eq.  satisfy These roots are given byThe solution of Eq.  can be rFrom Eqs.  and 47)47), the Using the above equations, the bending moment d in Eq.  can be gIn addition, after using Eq. , the axiIn addition, the strain The boundary conditions after using Laplace transform can be written asUsing the above conditions, six linear equations are given as follows in matrix formatThe above linear equation system provides the unknown parameters 387.The Riemann-sum approximation technique is employed to get numerical results for the purposes of determining the temperature, the displacement, bending moment and the stress distributions in the time domain. Any functions in the Laplace domain can be converted to the time domain using the following relationSample transform functions. To show the degree of precision, the findings of our numerical inversion approach are compared with the accurate estimates.6 were used, namely 40. Table We will check two examples to illustrate the applicability of the suggested numerical inversion technique. We take the transformed function mentclass2pt{minim40. More importantly, by increasing processing effort by a linear amount, our inversion approach may make large improvements in accuracy. Our technique is thought to work well for a broader variety of functions.The enhanced approach can quickly achieve great accuracy without requiring large calculations. In addition, as compared to other algorithms with comparable high accuracy, the programming effort for the approach is modest16In order to demonstrate the previous analysis and to compare the theoretical results obtained in the previous sections, we now take a numerical example to give computational results. Silicon, as an anthropic substance, is the material chosen for this numerical evaluation. Thus, the physical properties of the problem are expressed in SI units asThe nanobeam aspect ratios are taken as tion Eq. . Mathema15. In this case 15, it was observed that the behavior of mechanical and thermal waves of varying magnitudes is relatively comparable.In this part, a comparison study will be carried out to assess the reliability and validity of the findings, as well as the correctness of the suggested model. Using Laplace transforms, the analytical formulae for all of the distinct physical fields were recovered in the preceding section. The Riemann-sum approximation approach is used to get numerical results. Table 15 are in good agreement, suggesting that our model is valid, as shown in Table The existence of non-local operator, ramping time and phase lag parameters in the behavior of thermal and mechanical waves is indicated by the data shown in the table. The findings of the current technique and those of Ref.Case I: Explore how the values of different non-local parameter entclass1pt{minimaCase II: Explain how the thermophysical fields change by varying the values of the different ramping time parameter 03 Figs. , 8, 9.Case III: A comparison is studied between the different thermoelastic models in the event that the values of the ramping time nt Figs. , 12, 13.Case IV: It shows the effect of the initial magnetic field coefficient These numerical calculations are made in the following four cases:The non-local theorem is proved by replacing the variables in the standard continuous medium with nonlocal coefficients and expressing internal forces as nonlocal expressions. When looking for mechanical behavior at the micro/nano-scale, other physical phenomena must also be taken into account. The physical qualities of the structure, as well as the possibilities of deformation, are influenced by physical events. At nanometer scales, the size effect is often prominent and its effect on the mechanical performance of the nanostructures must be explicitly addressed. Thus, magneto-electro-elastic nanostructuring has become an active research project that depends on its size. For this reason, we will study the effect of the nonlocal parameter on the distributions of different fields.In this case, three values for the non-local parameter entclass1pt{minima41, small-scale effects play a significant impact in the thermal and mechanical properties and responses of micro/nanostructures, according to the results of experiments. As a result, nonlocal influences should be addressed while analyzing the mechanical behavior of nanostructures.As reported byIt is clear from Fig. The gap between non-local and classical theories increases with the increase of the non-local parameter.The non-local parameter has a significant effect on the lateral vibration The decrease in deflection is more evident at higher levels and should not be disregarded. In summary, the small scale influence makes the nanobeam more flexible because elastic springs connect the atoms in nonlocal theory.The maximum deflection occurs at the center of the beam  From the figures, we can see the major influence on all the fields studied of the ramping time The third case illustrates an important comparison between the various nonlocal thermoelastic models as they can be obtained as special cases from the presented thermoelastic dual phase lag model (DPL). The classical theory of thermoelasticity (CTE) can be taken by putting the phase lag mentclasspt{minimaFrom these figures, we show that the classical thermoelasticity theory CTE is near to the DPL model of thermoelasticity. However, The LS model distributions differ from the DPL model distributions. We observed moreover as presented in Fig. 5, there are two relaxation times (phase lags), so the reduction is greater than in the case of Lord and Shulman model2.We note that due to the presence of relaxation times the speed of thermal and mechanical waves is reduced. We also note that in the case of the Tzou modelIn the last case, we have analyzed the influence of the initial magnetic field parameter The effect of the magnetic field on the responses of the nanobeam can be seen clearly in this situation. The amplitude of vibrations is significantly altered by raising the magnetic field value. The amplitude of deflection field vibration responses rises as the magnetic field increases see Fig. , but theA significant effect was observed for the non-local response compared to the absence of the non-local effect. The non-local coefficient has a prominent role in changing the studied physical fields except for temperature.The maximum deflection that occurs at the center of the beam while the ends meet the stipulated boundary criteria. The largest deflection occurs in the middle of the beam may be due to the simply supported boundary conditions.The crossing phenomena in displacement were demonstrated to be dependent on the linear coupling stiffness between the layers, as well as the nonlocal parameter.The wave propagation characteristics of nanobeams are affected by nonlocal scale parameter, temperature change, thermal load, and external magnetic potential, according to numerical results.All physical fields within the body are strongly affected by changing ramping time parameter. The temperature ramp rate can be used to model real-world temperature variations in aerospace structures.The variation of the field quantities is shown to have a significant effect on thermal-phases lag parameters. It is shown that in conventional thermoelasticity the profile of the physical field quantity is larger than in generalized models.These results obtained in this paper are important for the design of integrated functional devices and smart devices.The current work only analyses the nanostructure exposed to longitudinal magnetic field; in the future, it would be helpful to observe the nanostructure exposed to magnetic field in 3-dimensional directions.In this paper, thermal vibration analysis of nanobeams by considering the magneto-thermo-elastic effect was studied using the non-local elasticity of Eringen and the Euler–Bernoulli beam principle. Also, based on the two-phase delay thermoelastic model, the problem was also studied. The effect of a permanent magnetic field on the vibrations of nanobeams was modeled using the concept of the Lorentz force. The Laplace transformation method was used to calculate analytical findings for dimensionless temperature, deflection and thermal moment. Although the illustrations are self-explaining in showing the various peculiarities that occur during the wave spread, the following observations can be added.