Adaptive spectral smoothening for development of robust keyword spotting system

IET Signal ProcessingVolume 13, Issue 5 p. 544-550 Research ArticleFree Access Adaptive spectral smoothening for development of robust keyword spotting system Biswaranjan Pattanayak, Biswaranjan Pattanayak Department of Electronics and Communication Engineering, National Institute of Technology Patna, IndiaSearch for more papers by this authorJayant Kumar Rout, Jayant Kumar Rout Department of Electronics and Communication Engineering, National Institute of Technology Patna, IndiaSearch for more papers by this authorGayadhar Pradhan, Corresponding Author Gayadhar Pradhan gdp@nitp.ac.in Department of Electronics and Communication Engineering, National Institute of Technology Patna, IndiaSearch for more papers by this author Biswaranjan Pattanayak, Biswaranjan Pattanayak Department of Electronics and Communication Engineering, National Institute of Technology Patna, IndiaSearch for more papers by this authorJayant Kumar Rout, Jayant Kumar Rout Department of Electronics and Communication Engineering, National Institute of Technology Patna, IndiaSearch for more papers by this authorGayadhar Pradhan, Corresponding Author Gayadhar Pradhan gdp@nitp.ac.in Department of Electronics and Communication Engineering, National Institute of Technology Patna, IndiaSearch for more papers by this author First published: 01 July 2019 https://doi.org/10.1049/iet-spr.2019.0027Citations: 11AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract It is well known that a keyword spotting (KWS) system provides significantly reduced performance in mismatched training and test conditions. In this work, an approach is proposed for reducing the mismatches between the training and test speech due to speaker-related variabilities and environmental noises. In the proposed approach, the variational-mode decomposition is first performed on the short-term magnitude spectra to decompose it into a number of variational mode functions (VMFs) in an adaptive manner. Then, a sufficiently smoothed spectra are reconstructed by selecting only two lower frequency VMFs. When the KWS system is developed by using Mel frequency cepstral coefficients (MFCCs) extracted from the smoothed spectra, a significantly improved performance is observed for pitch and noise mismatched test conditions. To further suppress the mismatches due to the pitch and speaking rate of the speakers, data-augmented training based on explicit prosody modification is performed. The experimental results presented in this study show that data-augmented training further enhances the performance of the developed KWS. 1 Introduction Probing for the specific words or phrases from a speech signal with the aid of a machine is termed as the keyword spotting (KWS) system. With recent development in technology, everyone wants to acquire information by searching for some keywords. The speech-based KWS system can have many user applications such as spoken document indexing, speech to gesture conversion, controlling electronic devices using voice commands, voice-based dialling on phone, spoken-term detection, password verification in authentication systems, surveillance and information retrieval [1-4]. In the literature, several approaches have been proposed for the development of effective KWS systems. Those include: the garbage models applying the likelihood ratio [5, 6], the phoneme or syllable sub-word unit recognition [7], and the large vocabulary continuous speech recognition (LVCSR) system [8-11]. Among those approaches, the LVCSR-based KWS provides better performance. In the LVCSR-based KWS system, an application specific language model is used for producing the word lattices [8, 9, 11]. Over the years, different supervised and unsupervised approaches have been proposed for the development of effective KWS systems. The acoustic modelling based on the Gaussian mixture models (GMM), the deep neural networks (DNN) [12], the recurrent neural networks (RNN) predicting the states of hidden Markov models (HMM) [13, 14], the long-short-term memory (LSTM) network, the bidirectional LSTM [15, 16] have been explored for the development of effective KWS systems. In [17, 18], the convolutional neural networks (CNN) independent of the HMM framework is also explored for the development of KWS. The support vector machine (SVM) [19] and different alternative approaches are also reported in the literature for the development of KWS system [20-24]. Despite the use of the aforementioned state-of-the-art acoustic modelling and feature normalisation methods, the KWS systems provide significantly reduced performance when mismatches exist between the speech data used for training and testing of the KWS systems [25]. For any practical applications of a KWS system, it can be expected that there will be mismatches between training and test speech signals due to speaker-specific variabilities like pitch and speaking rate. For example, the pitch of child speakers ranges from 200 to 500 Hz whereas, the pitch of the adult speakers varies from 70 to 250 Hz. On the other hand, the speaking-rate of children is lower than the adults [26-28]. There are no sufficient works reported in the literature for the development of KWS system in pitch and speaking rate mismatched conditions. Further to this, the temporal and spectral characteristics of speech data collected in different environments deviate depending on the ambient noises present in the surrounding. The most commonly used spectral features like Mel frequency cepstral coefficients (MFCCs) captures those variabilities present in the speech data. Recently, a data-augmented training approach using explicit prosody modification is proposed in [25] for reducing mismatches due to pitch and speaking rate in KWS system. In that reported work, the prosody modified adult's speech signals are augmented with the original adult's speech during training. The testing is performed without applying prosody modification. The KWS system developed using MFCC as the front-end feature provides improved performance when KWS system is tested using children's speech data. For a given speech signal, the mismatch effects on different analysis frames vary non-uniformly depending on the underlying sound units, the stress given to the syllable and noise present in the environment. Consequently, the prosody modification reported in [25] is not sufficient for removing the effect of mismatches on the MFCC features. To address this issue, in the presented study, a data-adaptive spectral smoothing through variational-mode decomposition (VMD) is explored for removing the pitch and environmental-induced mismatches on the MFCC features. The given short-term magnitude spectra are decomposed using VMD into variational-mode functions (VMFs), where each VMF represents a narrow band of the frequency spectra. The first two VMFs, those correspond to the low-frequency oscillations, are then combined to reconstruct a sufficiently smoothed spectra free from the pitch effects. Therefore, the reconstructed spectra mostly represent the resonance of the vocal tract system. The ill-effect of noises on the magnitude spectra is also reduced significantly. In this study, we have also explored the data-augmented training with the spectral smoothing for further enhancing the performance. The experimental results presented in this work show that the proposed approach for the development of KWS system outperforms the approach presented in [25] under pitch as well as noise-mismatched test conditions. The remainder of this paper is organised as follows: Section 2 presents a brief review of the VMD and its significance for spectral smoothening and data-augmented training using prosody modification. Section 3 presents the experimental setup for the development of KWS systems in mismatched conditions. The experimental results are presented in Section 4. Finally, the paper is concluded in Section 5. 2 Proposed approach for addressing mismatches in KWS system In the following subsections, first, a review on the general concept of VMD is presented. Next, the significance of VMD for spectral smoothening is discussed. This is followed by a brief review of data-augmented training through prosody modification. 2.1 Variational-mode decomposition The VMD is a non-recursive, concurrent decomposition method that breaks a given signal into different modes termed as VMFs [29, 30]. Each VMF contains a narrow-band frequency of the input signal. The centre frequency of each VMF is also estimated as -norm. The centre frequencies are sparsity priors which help in the reconstruction of input signal . and are computed by solving the constrained variational problem using (1) [29-31]. (1)such that (2)where, , , p, and * represents the VMFs, the centre frequencies of the VMFs, total number of VMFs, Dirac distribution and convolution operator, respectively. The signal reconstruction constraint is addressed by using Lagrangian multipliers and the quadratic penalty factor . The convergence properties of the penalty term at a finite weight value and strict enforcement of constraint by the Lagrangian multiplier are used. The augmented Lagrangian is given by (3)By using augmented Lagrangian and the alternate direction method of multipliers optimisation framework, the VMFs and corresponding centre frequencies can be computed. After optimisation, the resultant updated modes in the frequency domain are computed as follows: (4)where , , and are the frequency domain representations of , , and , respectively. The modes in time domain, can be obtained from using the inverse Fourier transform. Similarly, the updated centre frequencies are optimised in Fourier domain as follows: (5)It locates the updated frequency which is at the centre of the mode power spectrum. 2.2 Significance of VMD for spectral smoothing Each VMF contains a higher frequency of oscillations than the preceding ones. Therefore, the first and second VMFs mostly contain a lower frequency of oscillation. As mentioned earlier, the higher frequency components in the short-term magnitude spectra correspond to the pitch periodicity, whereas the low-frequency oscillation represents the resonance structure of the vocal tract system. Therefore, a sufficiently smoothed spectra can be constructed by considering only the first two VMFs. It can be expected that the MFCC features extracted from the smoothed spectra will be less sensitive to the pitch and noise-induced mismatches. The log-compressed magnitude spectrum for a segment of voiced speech taken from a high-pitch children speaker is shown in Fig. 1 a. The magnitude spectrum reconstructed by considering first VMF, first two VMFs and first three VMFs are shown in Figs. 1 b–d, respectively. By comparing the original and reconstructed spectra, it is evident that a sufficiently smoothed spectra can be obtained by considering only the first two VMFs. The reconstructed spectrum (Fig. 1 c) also closely matches with the envelope of the original spectrum. The first VMF is over-smoothed. On the other hand, by incorporating more VMFs, the pitch-induced effect will increase. Therefore, in this study, the spectrum is reconstructed by using only first two VMFs. Fig. 1Open in figure viewerPowerPoint Illustrating the effect of spectral smoothing using VMD on the magnitude spectrum (a) Short-time magnitude spectrum of a voiced frame taken from a child speaker. Smoothed spectra obtained by considering the first, (b) VMF, (c) Two VMFs and, (d) Three VMFs 2.3 Data-augmented training The MFCC features extracted from the smoothed spectra are relatively less sensitive to the pitch and environment noises. However, the pitch and speaking rate-induced variabilities can be further reduced by data-augmented training [25]. To compare the significance of the spectral smoothing for KWS task, we have also explored the data-augmented training approach reported in [25]. The pitch of the given speech signal can be changed by re-sampling. The speaking rate can be increased (or decreased) by compressing (or expanding) the given speech signal. Over the years, several approaches have been proposed for modifying pitch and speaking rate of the speech signal. Here, prosody modification is done using the epochs (glottal closure instants) [25, 32]. The epochs are detected by finding the positive to negative zero crossings of the zero-frequency-filtered (ZFF) speech signal [33]. For prosody modification, the epochs corresponding to the voiced as well as the non-voiced regions are considered. The prosody modification is done by generating a new sequence of epochs as per the desired pitch or duration modification factors. The original epochs closest to the desired epochs are identified and the speech samples around the original epochs are then repositioned beginning from the respective newly generated epochs. In this process, some speech samples are either deleted or appended to fill the new epoch intervals. To do so, the required number of speech samples are only deleted from the tail portion. On the other hand, for inserting new samples, about 10% of the tail portion of the selected speech samples are suitably re-sampled and then appended at the end. For data-augmented training, the features obtained from the original speech and prosody modified signal with the desired pitch or duration modification are pooled for the development of the KWS system. It is to note that, the prosody modification is applied only during the training of the KWS system, whereas the testing is performed without applying prosody modification [25]. 3 Experimental setup The experimental setup used here for studying the effect of acoustic mismatches on the KWS system is presented in the following subsections. 3.1 Speech data and selection of keywords In this study, to simulate pitch and duration mismatches, the training of the KWS system is done using the adult's speech and testing is performed using children's speech. It is well known that pitch and speaking rate of the speech data varies depending on the age and gender of the speaker. The pitch of the child speakers range from 200 to 500 Hz and the speaking-rate of a child speaker is lower than an adult [27, 28]. On the other hand, the pitch of the adult speakers varies from 70 to 250 Hz. For all the experimental evaluations presented here, the WSJCAM0 British English adult's speech corpus [34] is used for training model parameters of the KWS system. The training speech contains 13.3 h of speech data. There are 6812 speech files taken from 80 male and female speakers. The adult test set (AD_test) is also derived from the same database. AD_test set contains 1336 speech files (2.9 h of speech data) from 32 speakers. The AD_train and AD_test set are kept as non-overlapping. To evaluate the effect of pitch and speaking rate mismatches on the KWS system, a children's speech dataset is derived from the PF-STAR British English speech corpus [35]. The children's test set (CH_test) contains 642 speech files from 133 speakers. The age of children speakers lies between 3 and 14 years. In this work, 16 kHz sampled speech data is used. Table 1. Lists of selected keywords common for adults and children dataset. The performances are evaluated using a separate set of and keywords No. of keywords List of keywords 10 ZERO, ONE, TWO, THREE, FOUR, FIVE, SIX, SEVEN EIGHT, NINE 20 ZERO, ONE, TWO, THREE, FOUR, FIVE, SIX, SEVEN EIGHT, NINE, TEN, NUMBER, YEAR, POINT, WITH THEY, THERE, BANK, PEOPLE, MONTH To evaluate mismatches due to ambient noises, three different noises namely babble, factory, and white noise from the NOISEX-92 database [36] are added to the AD_test and the CH_test speech files. The energy level of the noise was scaled such that the overall signal-to-noise ratio (SNR) of the noise added speech data remained as 20, 15, 10, or 5 dB. For each SNR level, the performance parameters are averaged over all considered noises. In this study, the training of the system is done using clean speech signals and testing is done using noisy speech signals. As given in Table 1, different sets of 10 and 20 keywords which are present in both CH_test and AD_test are selected for experimental evaluations of the KWS system. 3.2 Front-end feature extraction The speech data is pre-emphasised using a filter with pre-emphasis factor of 0.97. The pre-emphasised speech signal is processed in overlapping Hamming windows of 20 ms in duration with an overlap of 10 ms for short-time analysis. The 13-dimensional base MFCC features are extracted using a 40 channel Mel-filterbank. The base features are then time spliced considering a context size of nine frames. Next, using linear discriminant analysis and maximum likelihood linear transform, a 40-dimensional feature vector is derived. The cepstral mean and variance normalisation (CMVN) is then performed to generate zero mean unit variance distribution. This is followed by feature normalisation using feature-space maximum likelihood linear regression (fMLLR) [37]. In case of the spectral smoothening using VMD, the smoothed spectra are reconstructed using the first two VMFs. The MFCCs are then extracted from the smoothed spectra (VMD-MFCCs). The feature transformation and normalisation are performed on the VMD-MFCCs using the same procedure as explained above. 3.3 Specifications of the KWS system The KWS system is developed using the Kaldi toolkit [38]. In this study, the standard Kaldi recipe is employed. The Kaldi KWS system consists of two parts: a continuous speech recognition (CSR) module and a KWS module. The CSR module is used to decode the speech signal to generate the lattices. In the CSR module, context-dependent HMM framework is employed for acoustic modelling. The GMM and DNN are used to generate observation densities for the HMM states. For the GMM-HMM-based system, the context-dependent cross-word triphones are modelled using three-states HMM with eight diagonal covariance Gaussian components per state. The state-level alignments generated using the GMM-HMM system is used for initialising the training of DNN-HMM-based system. Each layer in the DNN architecture consisted of 1024 hidden nodes with tanh non-linearity. The total number of hidden layers is fixed at 8. The fMLLR-normalised feature vectors are again time-spliced with a context size of 9 frames during learning of the DNN-HMM system. The learning rate of parameters is initialised at 0.015 which is finally reduced to 0.002. The minibatch size is fixed as 512. The KWS module uses the lattices to derive the word-level indexes. Those indexes are employed for searching the keywords. 3.4 Performance metrics The accuracy of the developed KWS systems are evaluated employing three performance metrics namely, the probability of miss detection , the probability of false alarm and the term-weighted value [39]. As defined in the NIST 2013 spoken term detection [39], for a given keyword and detection threshold , the keyword-specific miss detection probability and false alarm probability are computed as follows: (6) (7)where is the number of correct detection of the keyword for , is the number of reference occurrences of the keyword , is the number of spurious detection for and is the number of non-target trials for the keyword in the speech data. is given by (8)where is the total duration of the speech data in seconds. Then, the keyword-specific TWV is computed as follows: (9)where is a predefined constant. Then, the probability of miss detection , the probability of false alarm and the TWV for a threshold are computed by averaging the keyword-specific values as follows: (10) (11) (12)where M is the total number of keywords and is a system dependent threshold that attains maximum value for the TWV. This may be noted that, the lower values for and imply better performance. On the other hand, the higher values for the signify better performance [39, 40]. The TWV of a perfect system is 1 and the larger value of false alarm probability drives the TWV value towards . 3.5 Performance comparison The relative improvement (RI) in the TWV of the proposed KWS system using VMD-MFCCs to the baseline is computed as follows: (13)where and are the TWV of the proposed KWS and the TWV of the baseline KWS, respectively. 4 Experimental results and discussions The performances of the developed KWS system without and with spectral smoothing employing VMD are presented in the following subsections. The performance of the KWS systems under pitch mismatched condition is first evaluated to demonstrate the significance of spectral smoothening using VMD. Performances are then evaluated under noisy test condition to study the robustness of VMD-MFCC to ambient noises. Finally, the impact of data-augmented training on the VMD-MFCC is presented. 4.1 Performance of VMD-MFCC under pitch mismatched test conditions Table 2. Experimental results illustrating the significance of VMD-MFCCs for addressing pitch mismatch in a KWS system. Performances are given with respect to the DNN-HMM-based KWS system for adult's (AD_test) and children's (CH_test) data Test data No of keywords Features Performance parameter AD_test 10 MFCC 0.057 0.00014 0.8064 0.71 VMD-MFCC 0.053 0.00013 0.8122 20 MFCC 0.072 0.00029 0.6370 7.47 VMD-MFCC 0.072 0.00024 0.6846 CH_test 10 MFCC 0.210 0.00310 −2.2189 68 VMD-MFCC 0.138 0.00157 −0.7115 20 MFCC 0.096 0.00210 −1.2135 81 VMD-MFCC 0.107 0.00112 −0.2313 The performance of the KWS system is evaluated employing MFCCs as well as VMD-MFCCs using and keywords. The abbreviation refer to the relative improvement in the TWV of the proposed KWS system using VMD-MFCCs to the baseline. The performances of the developed KWS systems in terms of , , and without and with employing spectral smoothing for pitch matched and mismatched test conditions are given in Table 2. In this study, the KWS systems are trained using speech data from adult speakers. The performances are evaluated for both AD_test and CH_test sets. The TWV of the KWS systems for 10 as well as 20 keyword sets is degraded for the CH_test set when compared with the AD_test set. As presented in Table 2, for the CH_test sets the VMD-MFCCs provide significantly improved performance than the MFCC features. The RI observed in the TWV for the VMD-MFCCs to the MFCCs is 68 and 81% for 10 and 20 keywords, respectively. It is worth to mention here that VMD-MFCCs also provide improved performance for AD_test set. In this study, this test condition is referred to as the matched test. Therefore, by applying spectral smoothening using VMD, the performances are not reduced for the matched test condition which is very much desirable for the development of an effective KWS system. 4.2 Performance of VMD-MFCC under noisy test conditions Table 3. Experimental results illustrating the robustness of VMD-MFCC to ambient noises. Performances are given with respect to the DNN-HMM-based KWS systems for adult's (AD_test) and children's (CH_test) test sets after adding different noises No of keyword SNR in dB Employed Features AD_test set CH_test set 10 20 MFCC 0.034 0.00047 0.7728 2.38 0.252 0.00741 −5.1874 68 VMD-MFCC 0.063 0.00016 0.7912 0.171 0.00249 −1.6613 15 MFCC 0.036 0.00052 0.7442 3.73 0.270 0.00920 −5.2477 45.68 VMD-MFCC 0.070 0.00022 0.7720 0.246 0.00360 −2.8501 10 MFCC 0.037 0.00063 0.6923 4.62 0.270 0.04866 −9.4614 45.44 VMD-MFCC 0.070 0.00020 0.7243 0.351 0.00581 −5.1621 5 MFCC 0.041 0.00209 0.6511 2.94 0.408 0.01564 −13.1394 42.69 VMD-MFCC 0.069 0.00034 0.6703 0.631 0.00790 −7.5290 20 20 MFCC 0.190 0.00062 0.6172 1.6 0.174 0.00267 −1.8335 62 VMD-MFCC 0.075 0.00025 0.6272 0.143 0.00155 −0.6941 15 MFCC 0.211 0.00082 0.5707 3.78 0.304 0.00371 −2.9808 51.73 VMD-MFCC 0.085 0.00026 0.5923 0.173 0.00223 −1.4386 10 MFCC 0.731 0.00097 0.5171 6.6 0.399 0.00053 −4.7934 44.21 VMD-MFCC 0.090 0.00035 0.5512 0.336 0.00333 −2.674 5 MFCC 0.267 0.00040 0.4737 3.69 0.632 0.04214 −6.6714 40.79 VMD-MFCC 0.100 0.00040 0.4912 0.646 0.00430 −3.950 The performance of the KWS system is evaluated employing MFCC as well as VMD-MFCC features. The abbreviation refer to the relative improvement in the TWV of the proposed KWS system using VMD-MFCCs to the baseline. As discussed earlier, the high-frequency VMFs mostly contain the pitch periodicity. In the earlier reported works [26, 41], the VMD is demonstrated as an effective approach for suppressing the high frequency noises. Motivated by this, in this study, we have evaluated the robustness of the VMD-MFCCs for the task of KWS. The performances of the developed KWS systems using MFCCs and VMD-MFCCs under noisy test conditions are given in Table 3. In this study, the KWS systems are trained using original speech data from adult speakers. The performances are then evaluated by adding different noises to the AD_test and CH_test sets. The performances of the KWS systems employing MFCCs and VMD-MFCCs in terms of , , and are summarised in Table 3. For a particular SNR level, the performances are averaged over all the considered noises. The TWV of the KWS systems for 10 as well as 20 keyword sets degraded significantly for noisy test conditions. For both AD_test and CH_test sets, the performances of the KWS systems are reduced with the SNR level. Improved performance is observed for the VMD-MFCCs for the noisy test conditions. For a better comparison of both kind of features in each SNR level, the RI observed in the TWV of the VMD-MFCCs to the MFCCs is also given in the table. By comparing the RIs in the TWV, it can be seen that performance improvement in the CH_test set is significantly higher than the AD_test set for a particular SNR level. These experimental results show that the proposed spectral smoothening removes pitch periodicity significantly and also the ill-effect of noises on the MFCC features up to an extent. 4.3 Effect of data-augmented training on VMD-MFCC features The experimental results presented in Table 2 show that better KWS performances are observed for the VMD-MFCCs. However, significantly reduced performances are observed for the CH_test sets when compared with the AD_test sets. As discussed earlier, the speech data of child speakers differ from the adults due to pitch and speaking rate. In the case of child speaker, formant scaling also occurs due to smaller vocal tract geometry [42, 43]. Child speakers have higher formant frequencies than adults. All these mismatches also vary within the child speakers depending on the age. The effect of formant scaling can be addressed up to an extent through vocal tract length normalisation (VTLN) [42, 44, 45]. Recently, the effectiveness of explicit prosody modification through pitch and duration modification is presented for the task of KWS using children speech [25]. As reported in that work, data-augmented training through prosody modification provides better performance than the VTLN. However, the data-augmented training through prosody modification is not sufficient for completely removing the mismatches between the two groups of speakers. Further to this, the performance also varies depending on the pitch and duration modification factors [25]. The selection of optimal value for prosody modification is data-dependent. The pitch effect is reduced up to a larger extent in the case of the VMD-MFCCs. Consequently, it is expected that the data-augmented training of the VMD-MFCCs will provide better performance than the MFCCs. Motivated by this, the effect of data-augmented training on the VMD-MFCCs is also studied. Table 4. Experimental results illustrating the significance of data-augmented training on the MFCC and VMD-MFCC features for the task of detecting keywords No of keywords Data aug. Employed features AD_test set CH_test set 10 W/O MFCC 0.057 0.00014 0.8064 0.71 0.210 0.00310 −2.2189 68 VMD-MFCC 0.053 0.00013 0.8122 0.138 0.00157 −0.7115 PS125 MFCC 0.052 0.00012 0.8313 2.67 0.141 0.00140 −0.5382 65 VMD-MFCC 0.049 0.00010 0.8535 0.129 0.00106 −0.1859 PS135 MFCC 0.054 0.00012 0.8292 0.14 0.117 0.00057 0.3094 19.74 VMD-MFCC 0.053 0.00012 0.8304 0.131 0.00059 0.3705 TS065 MFCC 0.055 0.00012 0.8278 6.92 0.108 0.00131 −0.4138 90.18 VMD-MFCC 0.056 0.00006 0.8851 0.136 0.00090 −0.0406 TS075 MFCC 0.053 0.00014 0.8108 7.05 0.113 0.00255 −1.6609 87 VMD-MFCC 0.054 0.00008 0.8680 0.125 0.00109 −0.2160 20 W/O MFCC 0.072 0.00029 0.6370 7.47 0.096 0.00210 −1.2135 81 VMD-MFCC 0.072 0.00024 0.6846 0.107 0.00112 −0.2313 PS125 MFCC 0.064 0.00020 0.7025 2.04 0.106 0.00102 −0.1285 186.69 VMD-MFCC 0.070 0.00021 0.7169 0.103 0.00079 0.1114 PS135 MFCC 0.066 0.00023 0.6612 0.92 0.100 0.00073 0.1743 39.87 VMD-MFCC 0.071 0.00026 0.6673 0.100 0.00066 0.2438 TS065 MFCC 0.068 0.00021 0.7184 6.73 0.099 0.00096 −0.0621 167.63 VMD-MFCC 0.068 0.00017 0.7668 0.106 0.00085 0.0420 TS075 MFCC 0.063 0.00022 0.7137 7.9 0.101 0.00173 −0.8277 108 VMD-MFCC 0.075 0.00016 0.7701 0.095 0.00084 0.0668 Performances are given with respect to the DNN-HMM-based KWS system for adult's