Discriminative subspace modeling of SNR and duration variabilities for robust speaker verification. (September 2017)

• Record Type: Journal Article
• Authors: Li, Na; Mak, Man-Wai; Lin, Wei-Wei; Chien, Jen-Tzung
• Is Part Of: Computer speech & language. Volume 45(2017)
• Page Start: 83
• Page End: 103
• Publication Date: 2017-09
• Subjects: Speaker verification -- Duration variation -- SNR mismatch -- Variational Bayes -- I-vector -- PLDA; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2017.04.001
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: Model SNR and duration variability of i-vectors in discriminative subspaces. Use variational Bayesian methods to infer the latent variable model that defines the SNR and duration subspaces. Perform better than PLDA, SNR-invariant PLDA and PLDA with uncertainty propagation on long test utterances. Abstract: Although i-vectors together with probabilistic LDA (PLDA) have achieved a great success in speaker verification, how to suppress the undesirable effects caused by the variability in utterance length and background noise level is still a challenge. This paper aims to improve the robustness of i-vector based speaker verification systems by compensating for the utterance-length variability and noise-level variability. Inspired by the recent findings that noise-level variability can be modeled by a signal-to-noise ratio (SNR) subspace and that duration variability can be modeled as additive noise in the i-vector space, we propose to add an SNR factor and a duration factor to the PLDA model. In this framework, we assume that i-vectors derived from utterances with comparable durations share similar duration-specific information and that i-vectors extracted from utterances within a narrow SNR range have similar SNR-specific information. Based on these assumptions, an i-vector can be represented as a linear combination of four components: speaker, SNR, duration, and channel. A variational Bayes algorithm is developed to infer this latent variable model via a discriminative subspace training procedure. In the testing stage, different variabilities are compensated for when computing the likelihood ratio. Experiments on Common Conditions 1 and 4 in NIST 2012 SRE show that the proposed model outperforms the conventional PLDA and SNR-invariant PLDA. Results also show that the proposed model performs better than the uncertainty-propagation PLDA (UP-PLDA) for long test utterances.