Réda Dehak

Front-End Factor Analysis for Speaker Verification

This paper presents an extension of our previous work which proposes a new speaker representation for speaker verification. In this modeling, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis. This space is named the total variability space because it models both speaker and channel variabilities. Two speaker verification systems are proposed which use this new representation. The first system is a support vector machine-based system that uses the cosine kernel to estimate the similarity between the input data. The second system directly uses the cosine similarity as the final decision score. We tested three channel compensation techniques in the total variability space, which are within-class covariance normalization (WCCN), linear discriminate analysis (LDA), and nuisance attribute projection (NAP). We found that the best results are obtained when LDA is followed by WCCN. We achieved an equal error rate (EER) of 1.12% and MinDCF of 0.0094 using the cosine distance scoring on the male English trials of the core condition of the NIST 2008 Speaker Recognition Evaluation dataset. We also obtained 4% absolute EER improvement for both-gender trials on the 10 s-10 s condition compared to the classical joint factor analysis scoring.

Support vector machines and Joint Factor Analysis for speaker verification

This article presents several techniques to combine between Support vector machines (SVM) and Joint Factor Analysis (JFA) model for speaker verification. In this combination, the SVMs are applied to different sources of information produced by the JFA. These informations are the Gaussian Mixture Model supervectors and speakers and Common factors. We found that using SVM in JFA factors gave the best results especially when within class covariance normalization method is applied in order to compensate for the channel effect. The new combination results are comparable to other classical JFA scoring techniques.

Unsupervised Methods for Speaker Diarization: An Integrated and Iterative Approach

In speaker diarization, standard approaches typically perform speaker clustering on some initial segmentation before refining the segment boundaries in a re-segmentation step to obtain a final diarization hypothesis. In this paper, we integrate an improved clustering method with an existing re-segmentation algorithm and, in iterative fashion, optimize both speaker cluster assignments and segmentation boundaries jointly. For clustering, we extend our previous research using factor analysis for speaker modeling. In continuing to take advantage of the effectiveness of factor analysis as a front-end for extracting speaker-specific features (i.e., i-vectors), we develop a probabilistic approach to speaker clustering by applying a Bayesian Gaussian Mixture Model (GMM) to principal component analysis (PCA)-processed i-vectors. We then utilize information at different temporal resolutions to arrive at an iterative optimization scheme that, in alternating between clustering and re-segmentation steps, demonstrates the ability to improve both speaker cluster assignments and segmentation boundaries in an unsupervised manner. Our proposed methods attain results that are comparable to those of a state-of-the-art benchmark set on the multi-speaker CallHome telephone corpus. We further compare our system with a Bayesian nonparametric approach to diarization and attempt to reconcile their differences in both methodology and performance.

A channel-blind system for speaker verification

The majority of speaker verification systems proposed in the NIST speaker recognition evaluation are conditioned on the type of data to be processed: telephone or microphone. In this paper, we propose a new speaker verification system that can be applied to both types of data. This system, named blind system, is based on an extension of the total variability framework. Recognition results with the proposed channel-independent system are comparable to state of the art systems that require conditioning on the channel type. Another advantage of our proposed system is that it allows for combining data from multiple channels in the same visualization in order to explore the effects of different microphones and collection environments.