Text-independent speaker identification using Gaussian mixture bigram models

Wei Ho Tsai, Chiwei Che, Wen-Whei Chang

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

1 Scopus citations

Abstract

In this paper, a novel speaker modeling technique based on Gaussian mixture bigram model (GMBM) is introduced and evaluated for text-independent speaker identification (speaker-ID). GMBM is a stochastic framework that explores the context or time dependency of continuous observations from an information source. In view of the fact that speech features are correlated between successive frames, we attempt to investigate if speaker-ID can be aided by modeling the spectral correlation in speech through the usage of GMBMs. The proposed method was evaluated on a 100-speaker speech database. Experimental results demonstrated that the error rate of speaker-ID could be greatly reduced by using GMBMs, compared to the conventional speaker-ID technique based on Gaussian mixture models (GMMs).

Original languageEnglish
Title of host publication6th International Conference on Spoken Language Processing, ICSLP 2000
PublisherInternational Speech Communication Association
ISBN (Electronic)7801501144, 9787801501141
StatePublished - 1 Jan 2000
Event6th International Conference on Spoken Language Processing, ICSLP 2000 - Beijing, China
Duration: 16 Oct 200020 Oct 2000

Publication series

Name6th International Conference on Spoken Language Processing, ICSLP 2000

Conference

Conference6th International Conference on Spoken Language Processing, ICSLP 2000
CountryChina
CityBeijing
Period16/10/0020/10/00

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    Tsai, W. H., Che, C., & Chang, W-W. (2000). Text-independent speaker identification using Gaussian mixture bigram models. In 6th International Conference on Spoken Language Processing, ICSLP 2000 (6th International Conference on Spoken Language Processing, ICSLP 2000). International Speech Communication Association.