Modified group delay feature based total variability space modelling for speaker recognition

Previous researches show that the approaches based on the total variability space (TVS) followed by Gaussian probabilistic linear discriminant analysis (GPLDA) work effectively for dealing with convolutional noise (such as channel noise) and can bring some degree of gains in term of accuracy under additive noisy environment as well. However they meet difficulty while many types of noises are unseen and non-stationary in real world. To address this issue, we introduce the robust principal component analysis (RPCA) into the TVS modeled speaker verification system, called RPCA-TVS, which regards the noise spectrum as the low-rank component and the speech spectrum as the sparse component in short-time Fourier transform (SFT) domain. The highlighting of this paper is to improve the robustness of speaker verification under additive noisy environment, especially in non-stationary and unseen noise conditions. For evaluating the performance, we designed and generated an additive noisy corpus, based on the TIMIT and NUST603-2014 database, using the NaFT tools with 12 types of noise samples deriving from NOISEX-92 and FREESOUND. Experimental results demonstrate that the proposed RPCA-TVS can achieve better performance than the competing methods at various signal-to-noise ratio (SNR) levels. Especially, RPCA-TVS reduces the equal error rate (EER) by 5.12 % in average than the multi-condition system under additive noise conditions at SNR = 8 dB.

This paper presents use of hum of a person as a biometric cue for person recognition task. Mel Frequency Cepstral Coefficients (MFCC) is found to be state-of-the-art in the voice biometrics. However, it is magnitude-based features and ignores the phase information. This paper shows the effectiveness of phase-based information extracted via Modified Group Delay Function (MODGDF). The features developed by Mel filtering of MODGDF spectrum are called Modified Group Delay Cepstral Coefficients (MGDCC). The paper demonstrates two types of fusion strategies, viz., score-level and feature-level. The experimental results show that overall performance is improved by 3 % if a score-level fusion is employed between MFCC and MGDCC and 19.78 % by feature-level fusion in terms of % Equal Error Rate (EER). These experimental results clearly indicate that incorporating phase information along with magnitude-based features can effectively captures person-specific characteristics in humming.

This paper presents Program Guardian, which is a speaker recognition-based screening system for smart TV. The system identifies a specific person from his or her voice such that the smart TV can provide suitable programs for that person. This system is based on a robust speaker recognition system that uses robust principal component analysis (RPCA) and a sparse representation classifier (SRC). First, i-vectors that are generated from supervectors of Gaussian mixture models (GMMs) are used to generate the basic atoms of an over-complete dictionary. The i-vectors are then transformed using RPCA. The SRC is produced from transformed i-vector-based RPCA vectors. Finally, the sparse representation classifier corresponding to the target speaker with the least reconstruction error is constructed. NIST speaker recognition evaluation data base is used in our experiment. The results show that the proposed speaker recognition system is feasible and offers advantages over accuracy.

A new spectral estimator that exploits the simplicity and computational efficiency of the harmonic wavelet transform (HWT) for signal decomposition and the variance reduction property of the modified group delay (MGD), without any loss in frequency resolution, has been proposed. As the HWT directly provides the decimated subband components in the frequency domain, it enables direct application of the MGD to subband signals. In the HWT, the decomposition separates different parts of the spectrum into subbands and the decimation stretches each subband spectrum, hence the frequency resolution improves. Further as this also separates a low level spectral peak from a strong neighboring one, the signal detectability also improves. The MGD improves noise immunity, as it not only removes the spectral ripples due to leakage effect but also due to the associated noise. In view of these, the new estimator facilitates a significant improvement in: the reduction of variance, frequency resolution and signal detectability; compared with those of the MGD processing of fullband signals.

Recently, the speaker clustering approach exploiting the intra-conversation variability in the total variability space has shown promising performance. However, there exists the variability in different segments of the same speaker within a conversation, termed as intra-conversation intra-speaker variability, which may scatter the distribution of the corresponding i-vector based representation of short speech segment, and degrades the clustering performance. To address this issue, we propose a new speaker clustering approach based on an extended total variability factor analysis. In our proposed method, the intra-conversation total variability space is divided into the inter-speaker and intra-speaker variability space. And by explicitly compensating the intra-conversation intra-speaker variability, the short speech segments would be represented more accurately. To evaluate the effectiveness of the proposed method, we conduct extensive experiments on NIST SRE 2008 summed channel telephone dataset. The experimental results show that the proposed method clearly outperforms the other state-of-the-art speaker clustering techniques in terms of clustering error rate.

Spectral representation of speech is complete when both the Fourier transform magnitude and phase spectra are specified. In conventional speech recognition systems, features are generally derived from the short-time magnitude spectrum. Although the importance of Fourier transform phase in speech perception has been realized, few attempts have been made to extract features from it. This is primarily because the resonances of the speech signal which manifest as transitions in the phase spectrum are completely masked by the wrapping of the phase spectrum. Hence, an alternative to processing the Fourier transform phase, for extracting speech features, is to process the group delay function which can be directly computed from the speech signal. The group delay function has been used in earlier efforts, to extract pitch and formant information from the speech signal. In all these efforts, no attempt was made to extract features from the speech signal and use them for speech recognition applications. This is primarily because the group delay function fails to capture the short-time spectral structure of speech owing to zeros that are close to the unit circle in the z-plane and also due to pitch periodicity effects. In this paper, the group delay function is modified to overcome these effects. Cepstral features are extracted from the modified group delay function and are called the modified group delay feature (MODGDF). The MODGDF is used for three speech recognition tasks namely, speaker, language, and continuous-speech recognition. Based on the results of feature and performance evaluation, the significance of the MODGDF as a new feature for speech recognition is discussed

Over recent years, i-vector-based framework has been proven to provide state-of-the-art performance in speaker verification. Each utterance is projected onto a total factor space and is represented by a low-dimensional feature vector. Channel compensation techniques are carried out in this low-dimensional feature space. Most of the compensation techniques take the sets of extracted i-vectors as input. By constructing between-class covariance and within-class covariance, we attempt to minimize the between-class variance mainly caused by channel effect and to maximize the variance between speakers. In the real-world application, enrollment and test data from each user (or speaker) are always scarce. Although it is widely thought that session variability is mostly caused by channel effects, phonetic variability, as a factor that causes session variability, is still a matter to be considered. We propose in this paper a new i-vector extraction algorithm from the total factor matrix which we term component reduction analysis (CRA). This new algorithm contributes to better modelling of session variability in the total factor space. We reported results on the male English trials of the core condition of the NIST 2008 Speaker Recognition Evaluation (SREs) dataset. As measured both by equal error rate and the minimum values of the NIST detection cost function, 10–15 % relative improvement is achieved compared to the baseline of traditional i-vector-based system.

Recent research shows that the i-vector framework for speaker recognition can significantly benefit from phonetic information. A common approach is to use a deep neural network (DNN) trained for automatic speech recognition to generate a universal background model (UBM). Studies in this area have been done in relatively clean conditions. However, strong background noise is known to severely reduce speaker recognition performance. This study investigates a phonetically-aware i-vector system in noisy conditions. We propose a front-end to tackle the noise problem by performing speech separation and examine its performance for both verification and identification tasks. The proposed separation system trains a DNN to estimate the ideal ratio mask of the noisy speech. The separated speech is then used to extract enhanced features for the i-vector framework. We compare the proposed system against a multi-condition trained baseline and a traditional GMM-UBM i-vector system. Our proposed system provides an absolute average improvement of 8% in identification accuracy and 1.2% in equal error rate.

In this paper, we present a Mixture Linear Prediction based approach for robust Gammatone Cepstral Coefficients extraction (MLPGCCs). The proposed method provides performance improvement of Automatic Speaker Verification (ASV) using i-vector and Gaussian Probabilistic Linear Discriminant Analysis GPLDA modeling under transmission channel noise. The performance of the extracted MLPGCCs was evaluated using the NIST 2008 database where a single channel microphone recorded conversational speech. The system is analyzed in the presence of different channel transmission noises such as Additive White Gaussian (AWGN) and Rayleigh fading at various Signals to Noise Ratio (SNR) levels. The evaluation results show that the MLPGCCs features are a promising way for the ASV task. Indeed, the speaker verification performance using the MLPGCCs proposed features is significantly improved compared to the conventional Gammatone Frequency Cepstral Coefficients (GFCCs) and Mel Frequency Cepstral Coefficients (MFCCs) features. For speech signals corrupted with AWGN noise at SNRs ranging from (-5 dB to 15 dB), we obtain a significant reduction of the Equal Error Rate (EER) ranging from 9.41% to 6.65% and 3.72% to 1.50%, compared with conventional MFCCs and GFCCs features respectively. In addition, when the test speech signals are corrupted with Rayleigh fading channel we achieve an EER reduction ranging from 23.63% to 7.8% and from 10.88% to 6.8% compared with conventional MFCCs and GFCCs, respectively. We also found that the combination of GFCCs and MLPGCCs gives the highest performance of speaker verification system. The best performance combination achieved is around EER from 0.43% to 0.59% and 1.92% to 3.88%.

Speaker recognition is an active research area that contains notable usage in biometric security and authentication system. Currently, there exist many well-performing models in the speaker recognition domain. However, most of the advanced models implement deep learning that requires GPU support for real-time speech recognition, and it is not suitable for low-end devices. In this paper, we propose a lightweight text-independent speaker recognition model based on random forest classifier. It also introduces new features that are used for both speaker verification and identification tasks. The proposed model uses human speech based timbral properties as features that are classified using random forest. Timbre refers to the very basic properties of sound that allow listeners to discriminate among them. The prototype uses seven most actively searched timbre properties, boominess, brightness, depth, hardness, roughness, sharpness, and warmth as features of our speaker recognition model. The experiment is carried out on speaker verification and speaker identification tasks and shows the achievements and drawbacks of the proposed model. In the speaker identification phase, it achieves a maximum accuracy of 78%. On the contrary, in the speaker verification phase, the model maintains an accuracy of 80% having an equal error rate (ERR) of 0.24.

The mismatch between the training conditions and the test conditions severely degrades the performance of speaker verification. Aiming at solving this problem, this paper presents a method which uses a combination of improved nonnegative matrix factorization (IMNMF) and mel frequency cepstral coefficients (MFCC) with feature warping for improving identity-vector (i-vector) speaker verification performance in noisy environment. Unlike the traditional nonnegative matrix factorization (NMF), IMNMF uses extra free basis vectors to capture the features which are not included in training data, and linear constraints on dictionary atoms. Feature warping is used to remove channel noises. Therefore, the proposed method can reduce distortion of reconstructed speech while enhancing the recovered speech quality. The performance of i-vector speaker verification is evaluated by using the short utterance database and the NOISEX-92 database. The experiment results indicate that the score level fusion of feature-warped IMNMF-MFCC and feature-warped MFCC is superior to the baseline system at the equal error rate under the majority of signal-to-noise ratios (SNRs).

A text-dependent speaker verification system has been implemented for forensic application since 2008 when speech recording is allowed as legal evidence in Indonesian court. Due to the laborious procedures of the current system, an automatic text-independent speaker verification system is being developed in order to accelerate the forensic speaker verification process. The new forensic system is developed using a database spoken in Indonesian. The database is built on two scenarios, interview and conversation. This is done to simulate the real forensic condition for speaker verification in Indonesia. The system adopts the very famous Gaussian Mixture Model (GMM) and Universal Background Model (UBM) framework in speaker verification field and using Mel-Frequency Cepstral Coefficients (MFCC) as the speech feature. Along with the application of zero-normalization, the new forensic speaker verification system has successfully reached an Equal Error Rate (EER) of 4.66 % that is a better performance than the previous developed system of Indonesian forensic speaker verification.

This paper presents a comparison between several features normalization methods, and a comparison between different types of Gaussian Mixture Model (GMM) based supervectors normalizations for robust Speaker Verification. We implemented the methods of normalizations as a part of speaker verification system using Support Vector Machine (SVM) classifier and GMM-based supervectors. When implementing the speaker recognition system, we used Mel Frequency Cepstral Coefficients (MFCC) feature extraction. A valid question is which features normalization to use, if any. We examine the most common methods of feature normalizations, such as: Feature Warping mapping, and Cepstral Mean Subtraction (CMS) normalization with and without variance normalization. These methods were compared to features without normalization at all, and to a basic [-1, 1] normalization. In addition, we applied few types of normalizations to the GMM-mean supervectors, in order to improve the performance of the SVM classifier. All comparisons of the speaker verification system had been done in terms of DET curve, EER (Equal Error Rate) and Min. DCF. The best results we achieved were on combined supervector normalizations of Universal Background Model (UBM) Standard Deviation (STD) and [-1, 1] normalization. The type of the MFCC normalization has no big influence on the verification performance. The best results were: EER about 5.0% and MIN. DCF of 0.02.

I-vector Extraction for Speaker Recognition Based on Dimensionality Reduction

Language mismatching is considered one of the biggest challenges in achieving adequate speaker verification. The number of bilingual speakers worldwide is increasing, making speaker verification for speech technology more challenging. The main objective of this study is to examine the effect of language mismatch between training and test conditions on the performance of the speaker verification model, with a specific focus on children's speech by utilizing pre-trained and fine-tuned deep speaker embedding models and by investigating two child speech datasets (Samromur and kidsTALC). This work utilises two time-delay neural networks (TDNN): X-Vector and Emphasized Channel Attention, Propagation and Aggregation (ECAPA) to extract the embedding features of speech samples. For evaluation of the performance of speaker verification, we used the likelihood-ratio framework by using the likelihood-ratio score calculation method based on children’s voices and employing measures such as log-likelihood ratio cost (Cllr) and equal error rate (EER). The experimental results indicate that the language variety between training and testing utterances significantly degrades speaker verification performance compared to multi-language training; however, fine-tuning still performs better than pretrained models by 11.5% in the best performance of ECAPA-TDNN.