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Speaker and Gender Normalization for Continuous-Density Hidden Markov Models

Alex Acero and Xuedong Huang

Abstract

In this paper we describe a speaker-cluster normalization algorithm that we applied to both gendernormalization and speaker-normalization. To achieve parameter sharing the acoustic space is partitioned into classes. A maximum likelihood approach has been proposed under which the delta between the distribution mean and its corresponding acoustic class is mostly speaker-independent, whereas the means of the acoustic classes are mostly speaker-dependent. When applied to gender-normalization, the error rate reduction approaches that of a gender-dependent system but with half the number of parameters. For a speaker-normalized system, a 30% decrease in error rate was obtained in a batch recognition experiment in a context-dependent continuous-density HMM

Details

Publication typeInproceedings
Published inProc. of the Int. Conf. on Acoustics, Speech, and Signal
PublisherIEEE
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