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Feature Warping for Robust Speaker Verification

By Jason Pelecanos and Sridha Sridharan

Abstract

We propose a novel feature mapping approach that is robust to channel mismatch, additive noise and to some extent, non-linear effects attributed to handset transducers. These adverse effects can distort the short-term distribution of the speech features. Some methods have addressed this issue by conditioning the variance of the distribution, but not to the extent of conforming the speech statistics to a target distribution. The proposed target mapping method warps the distribution of a cepstral feature stream to a standardised distribution over a specified time interval.\ud We evaluate a number of the enhancement methods for speaker verification, and compare them against a Gaussian target mapping implementation. Results indicate improvements of the warping technique over a number of methods such as Cepstral Mean Subtraction (CMS), modulation spectrum processing, and short-term windowed CMS and variance normalisation

Topics: 080100 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING
Publisher: International Speech Communication Association (ISCA)
Year: 2001
OAI identifier: oai:eprints.qut.edu.au:10408

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