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

PDF file

In  Proc. of the Int. Conf. on Acoustics, Speech, and Signal

Publisher  IEEE
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