An hypothesized Wiener filtering approach to noisy speech recognition

Alberto D. Berstein, Ilan D. Shallom

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

37 Scopus citations

Abstract

The problem of speech recognition in a noisy environment is addressed. The mismatch problem originated when training a system in a clean environment and operating it in a noisy one. When measuring the similarity between a noisy test utterance and a list of clean templates a correction process, based on a series of Wiener filters built using the hypothesized clean template, is applied to the feature vectors of the noisy word. The filtering process is optimized as a by-product of the dynamic programming algorithm of the scoring step. Tests were conducted on two databases, one in Hebrew and the second in Japanese, using additive white and car noise at different SNRs. The method shows a good performance and compares well with other methods proposed in the literature.

Original languageEnglish
Title of host publicationProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Editors Anon
PublisherInstitute of Electrical and Electronics Engineers
Pages913-916
Number of pages4
ISBN (Print)078030033
StatePublished - 1 Dec 1991
EventProceedings of the 1991 International Conference on Acoustics, Speech, and Signal Processing - ICASSP 91 - Toronto, Ont, Can
Duration: 14 May 199117 May 1991

Publication series

NameProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Volume2
ISSN (Print)0736-7791

Conference

ConferenceProceedings of the 1991 International Conference on Acoustics, Speech, and Signal Processing - ICASSP 91
CityToronto, Ont, Can
Period14/05/9117/05/91

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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