Speaker and gender normalization for continuous-density hidden Markov models

Authors:
A. Acero;Xuedong Huang
Affiliations:
Microsoft Corp., Redmond, WA, USA;Microsoft Corp., Redmond, WA, USA
Venue:
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Year:
1996

Citing 0
Cited 3

A Probabilistic Model of Face Mapping with Local Transformations and Its Application to Person Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Voice-based gender identification in multimedia applications

Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
Pitch-based gender identification with two-stage classification

Security and Communication Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 data 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.