Journal ArticleDOI
Front-End Factor Analysis for Speaker Verification
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TLDR
An extension of the previous work which proposes a new speaker representation for speaker verification, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis, named the total variability space because it models both speaker and channel variabilities.Abstract:
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.read more
Citations
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X-Vectors: Robust DNN Embeddings for Speaker Recognition
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VoxCeleb2: Deep Speaker Recognition.
TL;DR: In this article, a large-scale audio-visual speaker recognition dataset, VoxCeleb2, is presented, which contains over a million utterances from over 6,000 speakers.
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Analysis of i-vector Length Normalization in Speaker Recognition Systems.
TL;DR: The proposed approach deals with the nonGaussian behavior of i-vectors by performing a simple length normalization, which allows the use of probabilistic models with Gaussian assumptions that yield equivalent performance to that of more complicated systems based on Heavy-Tailed assumptions.
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A time delay neural network architecture for efficient modeling of long temporal contexts.
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Deep neural networks for small footprint text-dependent speaker verification
TL;DR: Experimental results show the DNN based speaker verification system achieves good performance compared to a popular i-vector system on a small footprint text-dependent speaker verification task and is more robust to additive noise and outperforms the i- vector system at low False Rejection operating points.
References
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Journal ArticleDOI
Speaker Verification Using Adapted Gaussian Mixture Models
TL;DR: The major elements of MIT Lincoln Laboratory's Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs) are described.
Journal ArticleDOI
Joint Factor Analysis Versus Eigenchannels in Speaker Recognition
TL;DR: It is shown how the two approaches to the problem of session variability in Gaussian mixture model (GMM)-based speaker verification, eigenchannels, and joint factor analysis can be implemented using essentially the same software at all stages except for the enrollment of target speakers.
Journal ArticleDOI
A Study of Interspeaker Variability in Speaker Verification
TL;DR: It is shown that when a large joint factor analysis model is trained in this way and tested on the core condition, the extended data condition and the cross-channel condition, it is capable of performing at least as well as fusions of multiple systems of other types.
Proceedings ArticleDOI
SVM Based Speaker Verification using a GMM Supervector Kernel and NAP Variability Compensation
TL;DR: A support vector machine kernel is constructed using the GMM supervector and similarities based on this kernel between the method of SVM nuisance attribute projection (NAP) and the recent results in latent factor analysis are shown.