Open Access
Bayesian Speaker Verification with Heavy-Tailed Priors.
Patrick Kenny
- pp 14
TLDR
A new approach to speaker verification is described which is based on a generative model of speaker and channel effects but differs from Joint Factor Analysis in several respects, including each utterance is represented by a low dimensional feature vector rather than by a high dimensional set of Baum-Welch statistics.Abstract:
We describe a new approach to speaker verification which, like Joint Factor Analysis, is based on a generative model of speaker and channel effects but differs from Joint Factor Analysis in several respects. Firstly, each utterance is represented by a low dimensional feature vector, rather than by a high dimensional set of Baum-Welch statistics. Secondly, heavy-tailed distributions are used in place of Gaussian distributions in formulating the model, so that the effect of outlying data is diminished, both in training the model and at recognition time. Thirdly, the likelihood ratio used for making verification decisions is calculated (using variational Bayes) in a way which is fully consistent with the modeling assumptions and the rules of probability. Finally, experimental results show that, in the case of telephone speech, these likelihood ratios do not need to be normalized in order to set a trial-independent threshold for verification decisions. We report results on female speakers for several conditions in the NIST 2008 speaker recognition evaluation data, including microphone as well as telephone speech. As measured both by equal error rates and the minimum values of the NIST detection cost function, the results on telephone speech are about 30% better than we have achieved using Joint Factor Analysis.read more
Citations
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Proceedings ArticleDOI
X-Vectors: Robust DNN Embeddings for Speaker Recognition
TL;DR: This paper uses data augmentation, consisting of added noise and reverberation, as an inexpensive method to multiply the amount of training data and improve robustness of deep neural network embeddings for speaker recognition.
Proceedings Article
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.
Proceedings ArticleDOI
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.
Proceedings ArticleDOI
Front-End Factor Analysis For Speaker Verification
TL;DR: This paper investigates which configuration and which parameters lead to the best performance of an i-vectors/PLDA based speaker verification system and presents at the end some preliminary experiments in which the utterances comprised in the CSTR VCTK corpus were used besides utterances from MIT-MDSVC for training the total variability covariance matrix and the underlying PLDA matrices.
Proceedings ArticleDOI
Deep Neural Network Embeddings for Text-Independent Speaker Verification.
TL;DR: It is found that the embeddings outperform i-vectors for short speech segments and are competitive on long duration test conditions, which are the best results reported for speaker-discriminative neural networks when trained and tested on publicly available corpora.
References
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TL;DR: 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.
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Journal ArticleDOI
A Study of Interspeaker Variability in Speaker Verification
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