On subband-based blind separation for noisy speech recognition

Hyung Min Park, Ho Young Jung, Soo Young Lee, Te Won Lee

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

1 Scopus citations

Abstract

A method for denoising noisy speech signals in the feature extraction process for robust speech recognition is proposed. The method uses independent component analysis, in which a noise signal is linearly separated from two noisy speech microphone recordings. In addition, the method is optimized by computing a modified band that sums up FFT point values in several divided ranges of one band, and computes each band energy using the summed values. Thus, the number of unmixing networks is reduced. For instantaneous mixtures of speech and noise, the method showed the same recognition performance as for the clean speech signal case. For noisy speech signals recorded in real environments, the recognition rate was considerably increased after separation and the methods was particularly effective for a very low signal to noise ratio.

Original languageEnglish
Title of host publicationICONIP 1999, 6th International Conference on Neural Information Processing - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages204-209
Number of pages6
ISBN (Electronic)0780358716, 9780780358713
DOIs
StatePublished - 1999
Event6th International Conference on Neural Information Processing, ICONIP 1999 - Perth, Australia
Duration: 16 Nov 199920 Nov 1999

Publication series

NameICONIP 1999, 6th International Conference on Neural Information Processing - Proceedings
Volume1

Conference

Conference6th International Conference on Neural Information Processing, ICONIP 1999
Country/TerritoryAustralia
CityPerth
Period16/11/9920/11/99

Keywords

  • independent component analysis
  • noise robustness
  • Speech recognition

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