A GMM-Based handset selector for channel mismatch compensation with applications to speaker identification

K. K. Yiu, M. W. Mak, S. Y. Kung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In telephone-based speaker identification, variation in handset characteristics can introduce severe speech variability even for speech uttered by the same speaker. This paper proposes a method to compensate the variation in handset characteristics. In the method, a number of Gaussian mixture models are independently trained to identify the most likely handset given a test utterance. The identified handset is used to select a compensation vector from a set of pre-computed vectors, where the pre-computed vectors are the average frame-by-frame differences between the clean and distorted utterances. The clean features are then recovered by subtracting the selected compensation vector from the distorted vectors. Experimental results based on 138 speakers of the YOHO and telephone YOHO corpora show that the proposed approach is computationally efficient and is able to increase the accuracy from 17% (without compensation) to 85% (with compensation).

Original languageEnglish (US)
Title of host publicationAdvances in Multimedia Information Processing - PCM 2001 - 2nd IEEE Pacific Rim Conference on Multimedia, Proceedings
EditorsHeung-Yeung Shum, Mark Liao, Shih-Fu Chang
PublisherSpringer Verlag
Pages1132-1137
Number of pages6
ISBN (Print)3540426809, 9783540426806
DOIs
StatePublished - Jan 1 2001
Externally publishedYes
Event2nd IEEE Pacific-Rim Conference on Multimedia, IEEE-PCM 2001 - Beijing, China
Duration: Oct 24 2001Oct 26 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2195
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd IEEE Pacific-Rim Conference on Multimedia, IEEE-PCM 2001
CountryChina
CityBeijing
Period10/24/0110/26/01

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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