A fast algorithm for stochastic matching with application to robust speaker verification

Acoustic mismatch between training and test environments is one of the major problems in telephone-based speaker recognition. Speaker recognition performances are degraded when an HMM trained under one set of conditions is used to evaluate data collected from different telephone channels, microphones, etc. The mismatch can be approximated as a linear transform in a cepstral domain. In this paper, we present a fast, efficient algorithm to estimate the parameters of the linear transform for real-time applications. Using the algorithm, test data are transformed toward the training conditions by rotation, scale, and translation without, destroying the the detailed characteristics of speech, then, speaker dependent HMM´s can be used to evaluate the details under the same condition as training. Compared to cepstral mean subtraction (CMS) and other bias removal techniques, the proposed linear transform is more general since CMS and others only consider translation; compared to maximum-likelihood approaches for stochastic matching, the proposed algorithm is simpler and faster since iterative techniques are not required. The proposed algorithm improves the performance of a speaker verification system in the experiments reported in this paper