Employment of Subspace Gaussian Mixture Models in Speaker Recognition

conference paper

English

This paper presents Subspace Gaussian Mixture Model (SGMM) approach employed as a probabilistic generative model to estimate speaker vector representations to be subsequently used in the speaker verification task. SGMMs have already been shown to significantly outperform traditional HMM/GMMs in Automatic Speech Recognition (ASR) applications. An extension to the basic SGMM framework allows to robustly estimate low-dimensional speaker vectors and exploit them for speaker adaptation. We propose a speaker verification framework based on low-dimensional speaker vectors estimated using SGMMs, trained in ASR manner using manual transcriptions. To test the robustness of the system, we evaluate the proposed approach with respect to the state-of-the-art i-vector extractor on the NIST SRE 2010 evaluation set and on four different length-utterance conditions: 3sec-10sec, 10 sec-30 sec, 30 sec-60 sec and full (untruncated) utterances. Experimental results reveal that while i-vector system performs better on truncated 3sec to 10sec and 10 sec to 30 sec utterances, noticeable improvements are observed with SGMMs especially on full length-utterance durations. Eventually, the proposed SGMM approach exhibits complementary properties and can thus be efficiently fused with i-vector based speaker verification system.

speaker recognition, i-vectors, subspace Gaussian mixture models, automatic speech recognition

MOTLÍČEK, P.; DEY, S.; MADIKERI, S.; BURGET, L.

2015

19. 4. 2015

IEEE Signal Processing Society

South Brisbane, Queensland

978-1-4673-6997-8

Proceedings of 2015 IEEE International Conference on Acoustics, Speech and Signal Processing

4445

4449

5

https://ieeexplore.ieee.org/document/7178811

motlicek_icassp2015_0004445.pdf