Probabilistic compensation of unreliable feature components for robust speech recognition

Cyan L. Keung, Oscar C. Au, Chi H. Yim, Carrson C. Fung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Missing feature theory is well studied in robust ASR context, many works have been done on additive noise of different colors. These are based mainly on classical spectral subtraction and marginal density techniques. This paper addresses the problem of temporal distortion of feature components, that is all about time domain instead of frequency one. No specific noise model and extract computation needed. We showed that the digit words recognition rate is above 95%, given test samples are clean with 10dB white noise added to middle 30% portion of speech along the time axis.

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
Country/TerritoryChina
CityBeijing
Period16/10/0020/10/00

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