Cluster-Dependent Feature Transformation for Telephone-Based Speaker Verification

Chi Leung Tsang, Man Wai Mak, Sun Yuan Kung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)


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
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)
Publication statusPublished - 1 Dec 2003

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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