Mixtures of probabilistic principal component analyzers
Neural Computation
Explicit modelling of session variability for speaker verification
Computer Speech and Language
Support vector machines and Joint Factor Analysis for speaker verification
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Comparison of scoring methods used in speaker recognition with Joint Factor Analysis
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
An overview of text-independent speaker recognition: From features to supervectors
Speech Communication
Analysis of Feature Extraction and Channel Compensation in a GMM Speaker Recognition System
IEEE Transactions on Audio, Speech, and Language Processing
Front-End Factor Analysis for Speaker Verification
IEEE Transactions on Audio, Speech, and Language Processing
A Study of Interspeaker Variability in Speaker Verification
IEEE Transactions on Audio, Speech, and Language Processing
Joint Factor Analysis Versus Eigenchannels in Speaker Recognition
IEEE Transactions on Audio, Speech, and Language Processing
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We present a comparison of speaker verification systems based on unsupervised and supervised mixtures of probabilistic linear discriminant analysis (PLDA) models. This paper explores current applicability of unsupervised mixtures of PLDA models with Gaussian priors in a total variability space for speaker verification. Moreover, we analyze the experimental conditions under which this application is advantageous, taking into account the existing limitations of training database sizes, provided by the National Institute of Standards and Technology (NIST). We also present a full derivation of the Maximum Likelihood learning procedure for PLDA mixture. Experimental results for a cross-channel NIST Speaker Recognition Evaluation (SRE) 2010 verification task show that unsupervised PLDA mixture is more effective than other state-of-the-art methods. We show that for this task a combination of a homogeneous i-vector extractor and a mixture of two Gaussian PLDA models is more effective than a cross-channel i-vector extractor with a single Gaussian PLDA.