10th Annual Conference of the International Speech Communication Association

Brighton, United Kingdom
September 6-10, 2009

Fast GMM Computation for Speaker Verification Using Scalar Quantization and Discrete Densities

Guoli Ye (1), Brian Mak (1), Man-Wai Mak (2)

(1) Hong Kong University of Science & Technology, China
(2) Hong Kong Polytechnic University, China

Most of current state-of-the-art speaker verification (SV) systems use Gaussian mixture model (GMM) to represent the universal background model (UBM) and the speaker models (SM). For an SV system that employs log-likelihood ratio between SM and UBM to make the decision, its computational efficiency is largely determined by the GMM computation. This paper attempts to speedup GMM computation by converting a continuous-density GMM to a single or a mixture of discrete densities using scalar quantization. We investigated a spectrum of such discrete models: from high-density discrete models to discrete mixture models, and their combination called high-density discrete-mixture models. For the NIST 2002 SV task, we obtained an overall speedup by a factor of 2100 with little loss in EER performance.

Full Paper

Bibliographic reference.  Ye, Guoli / Mak, Brian / Mak, Man-Wai (2009): "Fast GMM computation for speaker verification using scalar quantization and discrete densities", In INTERSPEECH-2009, 2327-2330.