Search

• Proposed an unsupervised segment-based method for robust voice activity detection. • Proposed modified rVAD that uses computationally fast spectral flatness calculation. • Evaluated rVAD in terms of VAD performance using RATS and Aurora-2 databases. • Evaluated rVAD in terms of speaker verification performance using RedDots 2016. • rVAD showed favorable performance on various difficult tasks over existing methods. This paper presents an unsupervised segment-based method for robust voice activity detection (rVAD). The method consists of two passes of denoising followed by a voice activity detection (VAD) stage. In the first pass, high-energy segments in a speech signal are detected by using a posteriori signal-to-noise ratio (SNR) weighted energy difference and if no pitch is detected within a segment, the segment is considered as a high-energy noise segment and set to zero. In the second pass, the speech signal is denoised by a speech enhancement method, for which several methods are explored. Next, neighbouring frames with pitch are grouped together to form pitch segments, and based on speech statistics, the pitch segments are further extended from both ends in order to include both voiced and unvoiced sounds and likely non-speech parts as well. In the end, a posteriori SNR weighted energy difference is applied to the extended pitch segments of the denoised speech signal for detecting voice activity. We evaluate the VAD performance of the proposed method using two databases, RATS and Aurora-2, which contain a large variety of noise conditions. The rVAD method is further evaluated, in terms of speaker verification performance, on the RedDots 2016 challenge database and its noise-corrupted versions. Experiment results show that rVAD is compared favourably with a number of existing methods. In addition, we present a modified version of rVAD where computationally intensive pitch extraction is replaced by computationally efficient spectral flatness calculation. The modified version significantly reduces the computational complexity at the cost of moderately inferior VAD performance, which is an advantage when processing a large amount of data and running on low resource devices. The source code of rVAD is made publicly available. [ABSTRACT FROM AUTHOR]

• Automatic speaker verification (ASV) to find targets for mimicry attacks. • Attacker's ASV system differs from the attacked ASV system. • Six locally recruited attackers, 7365 potential targets from VoxCeleb. • Impersonators were not successful in attacking ASV systems. • Broad rank of speakers generalizes from attacker's ASV to attacked ASV. In this work, we simulate a scenario, where a publicly available ASV system is used to enhance mimicry attacks against another closed source ASV system. In specific, ASV technology is used to perform a similarity search between the voices of recruited attackers (6) and potential target speakers (7,365) from VoxCeleb corpora to find the closest targets for each of the attackers. In addition, we consider 'median', 'furthest', and 'common' targets to serve as a reference points. Our goal is to gain insights how well similarity rankings transfer from the attacker's ASV system to the attacked ASV system, whether the attackers are able to improve their attacks by mimicking, and how the properties of the voices of attackers change due to mimicking. We address these questions through ASV experiments, listening tests, and prosodic and formant analyses. For the ASV experiments, we use i-vector technology in the attacker side, and x-vectors in the attacked side. For the listening tests, we recruit listeners through crowdsourcing. The results of the ASV experiments indicate that the speaker similarity scores transfer well from one ASV system to another. Both the ASV experiments and the listening tests reveal that the mimicry attempts do not, in general, help in bringing attacker's scores closer to the target's. A detailed analysis shows that mimicking does not improve attacks, when the natural voices of attackers and targets are similar to each other. The analysis of prosody and formants suggests that the attackers were able to considerably change their speaking rates when mimicking, but the changes in F0 and formants were modest. Overall, the results suggest that untrained impersonators do not pose a high threat towards ASV systems, but the use of ASV systems to attack other ASV systems is a potential threat. [ABSTRACT FROM AUTHOR]

Automatic speaker verification (ASV) systems are highly vulnerable against spoofing attacks. Anti-spoofing, determining whether a speech signal is natural/genuine or spoofed, is very important for improving the reliability of the ASV systems. Spoofing attacks using the speech signals generated using speech synthesis and voice conversion have recently received great interest due to the 2015 edition of Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2015). In this paper, we propose to use linear prediction (LP) residual based features for anti-spoofing. Three different features extracted from LP residual signal were compared using the ASVspoof 2015 database. Experimental results indicate that LP residual phase cepstral coefficients (LPRPC) and LP residual Hilbert envelope cepstral coefficients (LPRHEC) obtained from the analytic signal of the LP residual yield promising results for anti-spoofing. The proposed features are found to outperform standard Mel-frequency cepstral coefficients (MFCC) and Cosine Phase (CosPhase) features. LPRPC and LPRHEC features give the smallest equal error rates (EER) for eight spoofing methods out of ten spoofing attacks in comparison to MFCC and CosPhase features. [ABSTRACT FROM AUTHOR]

Speaker verification (SV) systems involve mainly two individual stages: feature extraction and classification. In this paper, we explore these two modules with the aim of improving the performance of a speaker verification system under noisy conditions. On the one hand, the choice of the most appropriate acoustic features is a crucial factor for performing robust speaker verification. The acoustic parameters used in the proposed system are: Mel Frequency Cepstral Coefficients, their first and second derivatives (Deltas and Delta–Deltas), Bark Frequency Cepstral Coefficients, Perceptual Linear Predictive, and Relative Spectral Transform Perceptual Linear Predictive. In this paper, a complete comparison of different combinations of the previous features is discussed. On the other hand, the major weakness of a conventional support vector machine (SVM) classifier is the use of generic traditional kernel functions to compute the distances among data points. However, the kernel function of an SVM has great influence on its performance. In this work, we propose the combination of two SVM-based classifiers with different kernel functions: linear kernel and Gaussian radial basis function kernel with a logistic regression classifier. The combination is carried out by means of a parallel structure approach, in which different voting rules to take the final decision are considered. Results show that significant improvement in the performance of the SV system is achieved by using the combined features with the combined classifiers either with clean speech or in the presence of noise. Finally, to enhance the system more in noisy environments, the inclusion of the multiband noise removal technique as a preprocessing stage is proposed. [ABSTRACT FROM AUTHOR]

The task of speaker recognition is feasible when the speakers are co-operative or wish to be recognized. While modern automatic speaker verification (ASV) systems and some listeners are good at recognizing speakers from modal, unmodified speech, the task becomes notoriously difficult in situations of deliberate voice disguise when the speaker aims at masking his or her identity. We approach voice disguise from the perspective of acoustical and perceptual analysis using a self-collected corpus of 60 native Finnish speakers (31 female, 29 male) producing utterances in normal, intended young and intended old voice modes. The normal voices form a starting point and we are interested in studying how the two disguise modes impact the acoustical parameters and perceptual speaker similarity judgments. First, we study the effect of disguise as a relative change in fundamental frequency ( F 0) and formant frequencies ( F 1 to F 4) from modal to disguised utterances. Next, we investigate whether or not speaker comparisons that are deemed easy or difficult by a modern ASV system have a similar difficulty level for the human listeners. Further, we study affecting factors from listener-related self-reported information that may explain a particular listener’s success or failure in speaker similarity assessment. Our acoustic analysis reveals a systematic increase in relative change in mean F 0 for the intended young voices while for the intended old voices, the relative change is less prominent in most cases. Concerning the formants F 1 through F 4, 29% (for male) and 30% (for female) of the utterances did not exhibit a significant change in any formant value, while the remaining  ∼ 70% of utterances had significant changes in at least one formant. Our listening panel consists of 70 listeners, 32 native and 38 non-native, who listened to 24 utterance pairs selected using rankings produced by an ASV system. The results indicate that speaker pairs categorized as easy by our ASV system were also easy for the average listener. Similarly, the listeners made more errors in the difficult trials. The listening results indicate that target (same speaker) trials were more difficult for the non-native group, while the performance for the non-target pairs was similar for both native and non-native groups. [ABSTRACT FROM AUTHOR]

Inspired by the success of Deep Neural Networks (DNN) in text-independent speaker recognition, we have recently demonstrated that similar ideas can also be applied to the text-dependent speaker verification task. In this paper, we describe new advances with our state-of-the-art i-vector based approach to text-dependent speaker verification, which also makes use of different DNN techniques. In order to collect sufficient statistics for i-vector extraction, different frame alignment models are compared such as GMMs, phonemic HMMs or DNNs trained for senone classification. We also experiment with DNN based bottleneck features and their combinations with standard MFCC features. We experiment with few different DNN configurations and investigate the importance of training DNNs on 16 kHz speech. The results are reported on RSR2015 dataset, where training material is available for all possible enrollment and test phrases. Additionally, we report results also on more challenging RedDots dataset, where the system is built in truly phrase-independent way. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the application of the sparse representation of frames of the speech signal for the speaker verification. It is lately shown that Sparse Representation Classification (SRC), is promising for speaker recognition. We bring evidence that the frame level sparse representation classification resembles process of speech recognition in human sensory system. Since the recognition of different voices (noises) helps individuals to immediately distinguish between the noise and the original speech signal, a noise aware system was designed. As a principal in the sparse representation, we argued the mutual coherence of the dictionary columns, called dictionary atoms, which is not efficiently considered in the already published SRC base speaker verification researches. To suppress the mutual coherence, we use a dictionary learning method to construct a dictionary with effective atoms. Our proposed Frame Level Sparse Representation Classification (FSRC), provides new insights to the SRC based speaker verification. We demonstrate that, in the SRC based speaker verification, using a dictionary whose atoms are orthogonal can be more extensible than a dictionary whose atoms are highly correlated, and that the mutual coherence suppression is even more effective than imposing strong orthogonality on the dictionary atoms. We consider the performance of state-of-the-art speaker recognition systems and the proposed method on NIST SRE 2004 data. Experimental results show that in comparison to baseline methods, when we have enough amount of information in the registration of targets, the proposed method improves the performance of speaker verification system in noisy conditions. [ABSTRACT FROM AUTHOR]

Currently, the majority of the state-of-the-art speaker verification systems are based on i-vector and PLDA; however, PLDA requires a huge volume of development data from multiple different speakers. This makes it difficult to learn PLDA parameters for a domain with scarce data. In this paper, we study and extend an effective transfer learning method based on Bayesian joint probability, in which the Kullback–Leibler (KL) divergence between the source domain and the target domain is added as a regularization factor. This method utilizes the development data from the source domain to help find the optimal PLDA parameters for the target domain. Specifically, speaker verification of short utterances can be viewed as a task in the domain with a limited amount of long utterances. Therefore, transfer learning for PLDA can also be adopted to learn discriminative information from other domains with a great deal of long utterances. Experimental results based on the NIST SRE and Switchboard corpus demonstrate that the proposed method offers a significant performance gain when compared with the traditional PLDA. [ABSTRACT FROM AUTHOR]

In speaker recognition tasks, one of the reasons for reduced accuracy is due to closely resembling speakers in the acoustic space. In order to increase the discriminative power of the classifier, the system must be able to use only the unique features of a given speaker with respect to his/her acoustically resembling speaker. This paper proposes a technique to reduce the confusion errors, by finding speaker-specific phonemes and formulate a text using the subset of phonemes that are unique, for speaker verification task using i-vector based approach. In this paper spectral features such as linear prediction cepstral co-efficients (LPCC), perceptual linear prediction co-efficients (PLP) and phase feature such as modified group delay are experimented to analyse the importance of speaker-specific-text in speaker verification task. Experiments have been conducted on speaker verification task using speech data of 50 speakers collected in a laboratory environment. The experiments show that the equal error rate (EER) has been decreased significantly using i-vector approach with speaker-specific-text when compared to i-vector approach with random-text using different spectral and phase based features. [ABSTRACT FROM AUTHOR]

The performance of state-of-the-art speaker verification in uncontrolled environment is affected by different variabilities. Short duration variability is very common in these scenarios and causes the speaker verification performance to decrease quickly while the duration of verification utterances decreases. Linear discriminant analysis (LDA) is the most common session variability compensation algorithm, nevertheless it presents some shortcomings when trained with insufficient data. In this paper we introduce two methods for session variability compensation to deal with short-length utterances on i-vector space. The first method proposes to incorporate the short duration variability information in the within-class variance estimation process. The second proposes to compensate the session and short duration variabilities in two different spaces with LDA algorithms (2S-LDA). First, we analyzed the behavior of the within and between class scatters in the first proposed method. Then, both proposed methods are evaluated on telephone session from NIST SRE-08 for different duration of the evaluation utterances: full (average 2.5 min), 20, 15, 10 and 5 s. The 2S-LDA method obtains good results on different short-length utterances conditions in the evaluations, with a EER relative average improvement of 1.58%, compared to the best baseline (WCCN[LDA]). Finally, we applied the 2S-LDA method in speaker verification under reverberant environment, using different reverberant conditions from Reverb challenge 2013, obtaining an improvement of 8.96 and 23% under matched and mismatched reverberant conditions, respectively. [ABSTRACT FROM AUTHOR]

A new speech feature extraction method called Mel Modified Group Delay coefficients (MMGDCs) is presented in this paper. In this method, the modified group delay spectrum detects the high formants frequencies, while the Mel filters select these desired formants in the high frequency regions. Also in this paper, a scores fusion approach is proposed between MMGDCs, Mel coefficients (MFCCs) and their extensions using the asymmetric tappers. The adaboost algorithm is used as strategy of this fusion. The performances evaluation of the proposed features and their extended variants are carried out on NIST 2000 corpus, and tested in both clean and simulated noisy conditions, using different noise categories extracted from the NOISEX-92 database. The obtained results show the superiority of the proposed MMGDCs against MFCCs in terms of error reduction, and the power of adaboost algorithm to make the fusion between MMGDCs and MFCCs better, especially under noisy environments. [ABSTRACT FROM AUTHOR]

Conventional speaker recognition systems use the Universal Background Model (UBM) as an imposter for all speakers. In this paper, speaker models are clustered to obtain better imposter model representations for speaker verification purpose. First, a UBM is trained, and speaker models are adapted from the UBM. Then, the k-means algorithm with the Euclidean distance measure is applied to the speaker models. The speakers are divided into two, three, four, and five clusters. The resulting cluster centers are used as background models of their respective speakers. Experiments showed that the proposed method consistently produced lower Equal Error Rates (EER) than the conventional UBM approach for 3, 10, and 30 seconds long test utterances, and also for channel mismatch conditions. The proposed method is also compared with the i-vector approach. The three-cluster model achieved the best performance with a 12.4% relative EER reduction in average, compared to the i-vector method. Statistical significance of the results are also given. [ABSTRACT FROM AUTHOR]

This paper presents a new domain compensation framework by using phonetically discriminative features which are extracted from domain-dependent deep neural networks (DNNs). The domain compensation can be applied in both unsupervised and supervised manner, depending on whether the domain information of the development data is provided or not in advance. In supervised manner, the DNNs are trained on the development speech recordings of each given domain separately. While in the unsupervised manner, the development datasets are first automatically clustered into different domains, by using the Gaussian Mixture Model mean supervectors which are generated from each of the speech recordings, DNNs are then trained on the resulting clusters. Finally, we compensate the domain variabilities during the target speaker modeling step using support vector machines, by feeding in statistical vectors which are derived from the discriminative features extracted from the domain-dependent DNNs. The main strength of our proposed framework is that it does not need any speaker labels in the development dataset, which makes the proposed framework of great advantage over the state-of-the-art techniques that need speaker labels to train inter-speaker and/or intra-speaker variability models or channel compensation. Three speaker verification systems are investigated to examine the effectiveness of this new framework. Experimental results on the NIST SRE 2010 task demonstrate competitive performances to the state-of-the-art techniques in an initial implementation of the proposed framework. [ABSTRACT FROM AUTHOR]

In recent years, the i-vector based framework has been proven to provide state-of-the-art performance in the speaker verification field. Each utterance is projected onto a total factor space and is represented by a low-dimensional i-vector. However, the degradation of performance in the i-vector space remains problematic and is commonly attributed to channel variability. Most techniques used for the channel compensation of the i-vectors, such as linear discriminant analysis (LDA) or probabilistic linear discriminant analysis (PLDA) aim to compensate for the variabilities caused by channel effects. However, in real-world applications, the duration of enrollment and test utterances by each user (speaker) are always very limited. In this paper, we demonstrate, from both analytical and experimental perspectives, that feature sparsity and imbalance widely exist in short utterances, in which case the conventional i-vector extraction algorithm, based on maximum likelihood estimation (MLE), may lead to over-fitting and decrease the performance of the speaker verification system, especially for short utterances. This prompted us to propose an improved i-vector extraction algorithm, which we term adaptive first-order Baum–Welch statistics analysis (AFSA). This new algorithm suppresses and compensates for the deviation from first-order Baum–Welch statistics caused by feature sparsity and imbalance. We reported results on the male telephone portion of the core trial condition (short2-short3) and other short time trial conditions (short2-10sec and 10sec-10sec) on NIST 2008 Speaker Recognition Evaluations (SREs) dataset. As measured both by Equal Error Rate (EER) and the minimum values of the NIST Detection Cost Function (minDCF), 10%–15% relative improvement is obtained compared to the baseline of traditional i-vector based system. [ABSTRACT FROM AUTHOR]

Speaker verification performance degrades when input speech is tested in different sessions over a long period of time chronologically. Common ways to alleviate the long-term impact on performance degradation are enrollment data augmentation, speaker model adaptation, and adapted verification thresholds. From a point of view in features of a pattern recognition system, robust features that are speaker-specific, and invariant with time and acoustic environments are preferred to deal with this long-term variability. In this paper, with a newly created speech database, CSLT-Chronos, specially collected to reflect the long-term speaker variability, we investigate the issues in the frequency domain by emphasizing higher discrimination for speaker-specific information and lower sensitivity to time-related, session-specific information. F -ratio is employed as a criterion to determine the figure of merit to judge the above two sets of information, and to find a compromise between them. Inspired by the feature extraction procedure of the traditional MFCC calculation, two emphasis strategies are explored when generating modified acoustic features, the pre-filtering frequency warping and the post-filtering filter-bank outputs weighting are used for speaker verification. Experiments show that the two proposed features outperformed the traditional MFCC on CSLT-Chronos. The proposed approach is also studied by using the NIST SRE 2008 database in a state-of-the-art, i-vector based architecture. Experimental results demonstrate the advantage of proposed features over MFCC in LDA and PLDA based i-vector systems. [ABSTRACT FROM AUTHOR]

Despite the great advances made in the speaker recognition field, like joint factor analysis (JFA) and i-vectors, there are still situations where the quality of the speech signals involved in a speaker verification (SV) trial are not good enough to take reliable decisions. This fact motivated us to investigate speech quality measures that are related to the SV performance. We analyzed measures like signal-to-noise ratio (SNR), modulation index, number of speech frames, jitter, shimmer, or likelihood of the data given the universal background model (UBM), JFA and probabilistic linear discriminant analysis models. Besides, we introduce a novel and promising measure based on the vector Taylor series (VTS) paradigm, used to adapt a clean GMM to noisy speech. We used Bayesian networks to combine these measures and produce a probabilistic reliability measure. We applied it to detect trials badly classified. We trained our Bayesian network on NIST SRE08 distorted with noise and reverberation and evaluated on a distorted version of SRE10. We found that, for noise, the best measures were SNR and modulation index; and for reverberation, the UBM likelihood. VTS based measures performed well for both types of distortions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a sub-vector based speaker characterization method for biometric speaker verification, where speakers are represented by uniform segmentation of their maximum likelihood linear regression (MLLR) super-vectors called m-vectors. The MLLR transformation is estimated with respect to universal background model (UBM) without any speech/phonetic information. We introduce two strategies for segmentation of MLLR super-vector: one is called disjoint and other is an overlapped window technique. During test phase, m-vectors of the test utterance are scored against the claimant speaker. Before scoring, m-vectors are post-processed to compensate the session variability. In addition, we propose a clustering algorithm for multiple-class wise MLLR transformation, where Gaussian components of the UBM are clustered into different groups using the concept of expectation maximization (EM) and maximum likelihood (ML). In this case, MLLR transformations are estimated with respect to each class using the sufficient statistics accumulated from the Gaussian components belonging to the particular class, which are then used for m-vector system. The proposed method needs only once alignment of the data with respect to the UBM for multiple MLLR transformations. We first show that the proposed multi-class m-vector system shows promising speaker verification performance when compared to the conventional i-vector based speaker verification system. Secondly, the proposed EM based clustering technique is robust to the random initialization in-contrast to the conventional K-means algorithm and yields system performance better/equal which is best obtained by the K-means. Finally, we show that the fusion of the m-vector with the i-vector further improves the performance of the speaker verification in both score as well as feature domain. The experimental results are shown on various tasks of NIST 2008 speaker recognition evaluation (SRE) core condition. [ABSTRACT FROM AUTHOR]

Speaker verification (SV) using i-vector concept becomes state-of-the-art. In this technique, speakers are projected onto the total variability space and represented by vectors called i-vectors. During testing, the i-vectors of the test speech segment and claimant are conditioned to compensate for the session variability before scoring. So, i-vector system can be viewed as two processing blocks: one is total variability space and the other is post-processing module. Several questions arise, such as, (i) which part of the i-vector system plays a major role in speaker verification: total variability space or post-processing task; (ii) is the post-processing module intrinsic to the total variability space? The motivation of this paper is to partially answer these questions by proposing several simpler speaker characterization systems for speaker verification, where speakers are represented by their speaker characterization vectors (SCVs). The SCVs are obtained by uniform segmentation of the speakers gaussian mixture models (GMMs)- and maximum likelihood linear regression (MLLR) super-vectors. We consider two adaptation approaches for GMM super-vector: one is maximum a posteriori and other is MLLR. Similarly to the i-vector, SCVs are post-processed for session variability compensation during testing. The proposed system shows promising performance when compared to the classical i-vector system which indicates that the post-processing task plays an major role in i-vector based SV system and is not intrinsic to the total variability space. All experimental results are shown on NIST 2008 SRE core condition. [ABSTRACT FROM AUTHOR]

Recently, systems combining i-vector and probabilistic linear discriminant analysis (PLDA) have become one of the state-of-the-art methods in text-independent speaker verification. The training data of a PLDA model is often collected from a large, diverse population. However, including irrelevant or noisy training data may deteriorate the verification performance. In this paper, we first show that data selection using k -NN improves the speaker verification performance. We then present a robust way of selecting k based on the local distance-based outlier factor (LDOF). We call this method flexible k -NN ( fk -NN). We conduct experiments on male and female trials of several telephone conditions of the NIST 2006, 2008, 2010 and 2012 Speaker Recognition Evaluations (SRE). By using fk -NN, we discard a substantial amount of irrelevant or noisy training data without depending on tuning k , and achieve significant performance improvements on the NIST SRE sets. [ABSTRACT FROM AUTHOR]

Recently recognition of emotions became very important in the field of speech and/or speaker recognition. This paper is dedicated to experimental investigation of best acoustic features obtained for purpose of gender-dependent speaker recognition from emotional speech. Four feature sets - LPC (Linear Prediction Coefficients), LPCC (Linear Prediction Cepstral Coefficients), MFCC (Mel-frequency Cepstral Coefficients) and PLP (Perceptual linear prediction) coefficients - were compared in an experimental setup of speaker recognition system, based on i-vector representation. For evaluation of the system emotional speech recordings from newly created Slovak emotional database and Mahalanobis distance metric as scoring method were used. The results of the experiment showed the MFCC representation as the best fitted for speaker verification from Slovak emotional speech with recognition rate higher than 80%. [ABSTRACT FROM AUTHOR]

Most speaker recognition systems rely on short-term acoustic cepstral features for extracting the speaker-relevant information from the signal. But phonetic discriminant features, extracted by a bottle-neck multi-layer perceptron (MLP) on longer stretches of time, can provide a complementary information and have been adopted in speech transcription systems. We compare the speaker verification performance using cepstral features, discriminant features, and a concatenation of both followed by a dimension reduction. We consider two speaker recognition systems, one based on maximum likelihood linear regression (MLLR) super-vectors and the other on a state-of-the-art i-vector system with two session variability compensation schemes. Experiments are reported on a standard configuration of NIST SRE 2008 and 2010 databases. The results show that the phonetically discriminative MLP features retain speaker-specific information which is complementary to the short-term cepstral features. The performance improvement is obtained with both score domain and feature domain fusion and the speaker verification equal error rate (EER) is reduced up to 50% relative, compared to the best i-vector system using only cepstral features. [ABSTRACT FROM AUTHOR]

Humans are quite adept at communicating in presence of noise. However most speech processing systems, like automatic speech and speaker recognition systems, suffer from a significant drop in performance when speech signals are corrupted with unseen background distortions. The proposed work explores the use of a biologically-motivated multi-resolution spectral analysis for speech representation. This approach focuses on the information-rich spectral attributes of speech and presents an intricate yet computationally-efficient analysis of the speech signal by careful choice of model parameters. Further, the approach takes advantage of an information-theoretic analysis of the message and speaker dominant regions in the speech signal, and defines feature representations to address two diverse tasks such as speech and speaker recognition. The proposed analysis surpasses the standard Mel-Frequency Cepstral Coefficients (MFCC), and its enhanced variants (via mean subtraction, variance normalization and time sequence filtering) and yields significant improvements over a state-of-the-art noise robust feature scheme, on both speech and speaker recognition tasks. [ABSTRACT FROM AUTHOR]