Development of TEO phase for speaker recognition

It is a widely known fact that automatic speaker verification systems are quite vulnerable to replay speech. The present work deals with detecting replay speech by using the information available in glottal flow derivative (GFD) signal. In signal processing terms, the speech signal can be represented as the response of a vocal-tract system with excited by a excitation source in the form of glottal flow. The effect of record and replay devices distorted the spectral characteristics of the naturally uttered speech sample, resulting distortion in corresponding GFD signals. In this work the GFD signals are parameterized by using standard mel filters and Gaussian mixtures models are made for detection. Although various methods are available, by correlation analysis it is observed that in the context of the present work the dynamic programming phase slope algorithm (DYPSA) method is relatively more effective in estimating the GFD signals. The experimental studies are made on ASVSpoof2017 database. The proposed glottal flow derivative mel frequency cepstral coefficients (GFDMFCC) feature provides 20.53% equal error rate (EER). This performance is comparatively poor than by speech and residual based features. It is mainly due to the absence of fine structure information in estimated GFD signal. However, in fusion with speech signal based constant-Q cepstral coefficients (CQCC) features, the GFDMFCC feature provides an improvement of 10.30% with reference to conventional residual feature. This shows the usefulness of modelling GFD signals for detection of replay signals.

Detection of replay spoof speech using teager energy feature cues

It is widely known that the quality of confidence measure is critical for speech applications. In this paper, we present our recent work on improving word confidence scores by calibrating them using a small set of calibration data when only the recognized word sequence and associated raw confidence scores are made available. The core of our technique is the maximum entropy model with distribution constraints which naturally and effectively make use of the word distribution, the raw confidence-score distribution, and the context information. We demonstrate the effectiveness of our approach by showing that it can achieve relative 38% mean square error (MSE), 39% negative normalized likelihood (NNLL), and 23% equal error rate (EER) reduction on a voice mail transcription data set and relative 35% MSE, 45% NNLL, and 35% EER reduction on a command and control data set.

We discuss the limitations of the i-vector representation of speech segments in speaker recognition and explain how Joint Factor Analysis (JFA) can serve as an alternative feature extractor in a variety of ways. Building on the work of Zhao and Dong, we implemented a variational Bayes treatment of JFA which accommodates adaptation of universal background models (UBMs) in a natural way. This allows us to experiment with several types of features for speaker recognition: speaker factors and diagonal factors in addition to i-vectors, extracted with and without UBM adaptation in each case. We found that, in text-independent speaker verification experiments on NIST data, extracting i-vectors with UBM adaptation led to a 10% reduction in equal error rates although performance did not improve consistently over the whole DET curve. We achieved a further 10% reduction (with a similar inconsistency) by using speaker factors extracted with UBM adaptation as features. In text-dependent speaker recognition experiments on RSR2015 data, we were able to achieve very good performance using a JFA model with diagonal factors but no speaker factors as a feature extractor. Contrary to standard practice, this JFA model was configured so as to model speakerphrase combinations (rather than speakers) and it was trained on utterances of very short duration (rather than whole recording sessions). We also present a variant of the length normalization trick inspired by uncertainty propagation which leads to substantial gains in performance over the whole DET curve.

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%.

The automatic speaker verification (ASV) has achieved significant progress in recent years. However, it is still very challenging to generalize the ASV technologies to new, unknown and spoofing conditions. Most previous studies focused on extracting the speaker information from natural speech. This paper attempts to address the speaker verification from another perspective. The speaker identity information was exploited from singing speech. We first designed and released a new corpus for speaker verification based on singing and normal reading speech. Then, the speaker discrimination was compared and analyzed between natural and singing speech in different feature spaces. Furthermore, the conventional Gaussian mixture model, the dynamic time warping and the state-of-the-art deep neural network were investigated. They were used to build text-dependent ASV systems with different training-test conditions. Experimental results show that the voiceprint information in the singing speech was more distinguishable than the one in the normal speech. More than relative 20% reduction of equal error rate was obtained on both the gender-dependent and independent 1 s-1 s evaluation tasks.

Optimization of the parameters characterizing sigmoidal rate-level functions based on acoustic features

This study proposes an i-Vector based approach to gender identification. Gender-labeled utterances from the Fisher English (FE) corpus are used to formulate an i-Vector extraction framework, and a Probabilistic Linear Discriminant Analysis (PLDA) back-end is employed to compute the scores for gender identification. A novel duration mismatch compensation strategy is also presented that offers very little degradation in identification accuracy even with a large reduction in the duration of the test-segment. The proposed method is shown to consistently outperform a GMM-UBM based gender-identification scheme on several test-sets created from a held-out portion of the FE corpus, and is able to achieve an identification accuracy of up to 97.63%. On the severely distorted and multilingual DARPA-RATS (Robust Automatic Transcription of Speech) corpora, the proposed approach achieves an identification accuracy of 76.48% using only the FE data in training. Next, a novel unsupervised domain adaptation strategy is also presented that utilizes only unlabeled RATS data to adapt the out-of-domain PLDA parameters derived from the FE training data. The strategy is able to offer a 6.8% relative improvement in identification accuracy, and a 14.75% relative reduction in Equal Error Rate (EER) compared to using the out-of-domain PLDA model on the RATS test-utterances. These improvements are significant since: 1) RATS test-utterances are severely distorted, 2) No labeled data of any kind is used for 4 of the 5 languages present in the test-utterances.

In this paper appropriate confidence measures (CMs) are investigated for Mandarin command word recognition, both in the so-called target region and non-target region, respectively. Here the target region refers to the recognized speech part of command word while the non-target region refers to the recognized silence part. It shows that exploiting extra information in the non-target region can effectively complement the traditional CM which usually focus on the target region. Furthermore, when analyzing the non-target region in a more theoretical way, where Bayesian information criterion (BIC) is employed to locate more precise boundary in the non-target region, even more improvement is achieved. In two different Mandarin telephone command word tasks, more than 20% relative reduction of equal error rate (EER) is obtained.

Whereas most approaches to linear speech prediction fail to account for the quasi-periodic glottal flow, this paper incorporates a model for the glottal flow derivative (GFD) directly into the linear prediction problem. A linear model for the prediction error is obtained by constructing a dictionary of time-shifted GFD pulses. The pulses are constructed by applying glottal inverse filtering (GIF) to recorded speech. Minimizing the difference between the linear prediction residual and a sparse combination of the pulses in the dictionary leads to joint estimation of the linear predictor as well as a sparse representation for the prediction error that reveals the instants of vocal tract excitation (epochs). The method is applied to voiced segments extracted from the CMU Arctic dataset which also includes electro-glottograms. Results show that the proposed method is effective in estimating the parameters of interest and that GIF-based pulses more accurately model GFD pulses occurring in real speech than pulses computed using the mathematical models.

An automatic technique for estimating and modeling the glottal flow derivative source waveform from speech, and applying the model parameters to speaker identification, is presented. The estimate of the glottal flow derivative is decomposed into coarse structure, representing the general flow shape, and fine structure, comprising aspiration and other perturbations in the flow, from which model parameters are obtained. The glottal flow derivative is estimated using an inverse filter determined within a time interval of vocal-fold closure that is identified through differences in formant frequency modulation during the open and closed phases of the glottal cycle. This formant motion is predicted by Ananthapadmanabha and Fant (1982) to be a result of time-varying and nonlinear source/vocal tract coupling within a glottal cycle. The glottal flow derivative estimate is modeled using the Liljencrants-Fant (1986) model to capture its coarse structure, while the fine structure of the flow derivative is represented through energy and perturbation measures. The model parameters are used in a Gaussian mixture model speaker identification (SID) system. Both coarse- and fine-structure glottal features are shown to contain significant speaker-dependent information. For a large TIMIT database subset, averaging over male and female SID scores, the coarse-structure parameters achieve about 60% accuracy, the fine-structure parameters give about 40% accuracy, and their combination yields about 70% correct identification. Finally, in preliminary experiments on the counterpart telephone-degraded NTIMIT database, about a 5% error reduction in SID scores is obtained when source features are combined with traditional mel-cepstral measures.

Classification of Bisyllabic Lexical Stress Patterns Using Deep Neural Networks

For the first time in this paper we present results showing the effect of out of plane speaker head pose variation on a lip biometric based speaker verification system. Using appearance DCT based features, we adopt a Mutual Information analysis technique to highlight the class discriminant DCT components most robust to changes in out of plane pose. Experiments are conducted using the initial phase of a new multi view AudioVisual database designed for research and development of poseinvariant speech and speaker recognition. We show that verification performance can be improved by substituting higher order horizontal DCT components for vertical, particularly in the case of a train/test pose angle mismatch. We show that the best performance can be achieved by combining this alternative feature selection with multi view training, reporting a relative 45% Equal Error Rate reduction over a common energy based selection.

Speaker verification technology has widespread applications across many domains, benefiting from deep learning advancements. However, due to the high cost of acquiring labeled data, semi-supervised learning has emerged as a prominent research focus. Current semi-supervised learning frameworks commonly suffer from two limitations: (1) the labeled data distribution is often restricted, and (2) they still rely on a considerable amount of labeled data. To address these issues, we propose three different distribution scenarios of labeled data and construct a general semi-supervised framework. Furthermore, to enhance the guidance efficacy of limited labeled data, we innovatively employ prototype space optimization to strengthen the model's discriminative capability under low-resource scenarios. Experimental results demonstrate that on the Vox1-o test set, our approach achieves a 41.7% relative reduction in equal error rate compared to self-supervised baselines, and a 28.9% improvement over conventional semi-supervised framework baselines.

Forensic speaker verification systems show severe performance degradation in the presence of noise when the signal to noise ratio (SNR) is low. A possible solution to this problem is the use of multi-run independent component analysis (ICA) to reduce the effect of noise from the noisy speech signals. Previous works have used multi-run ICA in biosignal application; however, the effectiveness of multi-run ICA on noisy speaker verification has not been investigated yet. In this paper, we use multi-run ICA to enhance the noisy speech signals by choosing the highest signal to interference ratio (SIR) of the mixing matrix from different mixing matrices generated by iterating the fast ICA algorithm for several times. We use a combination of feature-warped mel frequency cepstral coefficients (MFCCs) and feature-warped MFCC extracted from the discrete wavelet transform (DWT) of the enhanced speech signals as the feature extraction. A state-of-the-art identity vector (i-vector) probabilistic linear discriminant analysis (PLDA) was used as a classifier in this paper. Experimental results demonstrate that the proposed method with multi-run ICA achieves high improvements in equal error rate (EER) of 66.68%, 69.24% and 70.78% over the baseline noisy speaker verification system, when the test speech signals are corrupted with CAR, STREET, and HOME noises respectively at −10 dB SNR.