Tree-structured speaker clustering for fast speaker adaptation

The paper proposes a tree-structured speaker clustering algorithm and discusses its application to fast speaker adaptation. By tracing the clustering tree from top to bottom, adaptation is performed step-by-step from global to local individuality of speech. This adaptation method employs successive...

Full description

Saved in:
Bibliographic Details
Main Authors: Kosaka, T., Sagayama, S.
Format: Conference Proceeding
Language:eng ; jpn
Subjects:
Clustering algorithms
Clustering methods
Error analysis
Hidden Markov models
Humans
Smoothing methods
Speech
Stochastic processes
Training data
Tree data structures
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The paper proposes a tree-structured speaker clustering algorithm and discusses its application to fast speaker adaptation. By tracing the clustering tree from top to bottom, adaptation is performed step-by-step from global to local individuality of speech. This adaptation method employs successive branch selection in the speaker clustering tree rather than parameter training and hence achieves fast adaptation using only a small amount of training data. This speaker adaptation method was applied to a hidden Markov network (HMnet) and evaluated in Japanese phoneme and phrase recognition experiments, in which it significantly outperformed speaker-independent recognition methods. In the phrase recognition experiments, the method reduced the error rate by 26.6% using three phrase utterances (approximately 2.7 seconds).< >
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.1994.389309