Cluster-Dependent Feature Transformation for Telephone-Based Speaker Verification

Chi Leung Tsang, Man Wai Mak, Sun-Yuan Kung

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

This paper presents a cluster-based feature transformation technique for telephone-based speaker verification when labels of the handset types are not available during the training phase. The technique combines a cluster selector with cluster-dependent feature transformations to reduce the acoustic mismatches among different handsets. Specifically, a GMM-based cluster selector is trained to identify the cluster that best represents the handset used by a claimant. Handset distorted features are then transformed by cluster-specific feature transformation to remove the acoustic distortion before being presented to the clean speaker models. Experimental results show that cluster-dependent feature transformation with number of clusters larger than the actual number of handsets can achieve a performance level very close to that achievable by the handset-based transformation approaches.

Original languageEnglish (US)
Pages (from-to)86-94
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2688
StatePublished - Dec 1 2003
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Cluster-Dependent Feature Transformation for Telephone-Based Speaker Verification'. Together they form a unique fingerprint.

Cite this