Speaker and Gender Normalization for Continuous-Density Hidden Markov Models
- Alex Acero ,
- Xuedong Huang
Proc. of the Int. Conf. on Acoustics, Speech, and Signal |
Published by IEEE
In this paper we describe a speaker-cluster normalization algorithm that we applied to both gender-normalization 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 system.
© 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.http://www.ieee.org/