Manifold HLDA and its application to robust speech recognition

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

Abstract

A manifold heteroscedastic linear discriminant analysis (MHLDA) which removes environmental information explicitly from the useful information for discrimination is proposed. Usually, a feature parameter used in pattern recognition involves categorical information and also environmental information. A well-known HLDA tries to extract useful information (UI) to represent categorical information from the feature parameter. However, environmental information is still remained in the UI parameters extracted by HLDA, and it causes slight degradation in performance. This is because HLDA does not handle the environmental information explicitly. The proposed MHLDA also tries to extract UI like HLDA, but it handles environmental information explicitly. This handling makes MHLDA-based UI parameter less influenced of environment. However, as compensation, in MHLDA, the categorical information is little bit destroyed. In this paper, we try to combine HLDA-based UI and MHLDA-based UI for pattern recognition, and draw benefit of both parameters. Experimental results show the effectiveness of this combining method.

Original languageEnglish
Title of host publicationINTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP
PublisherInternational Speech Communication Association
Pages1551-1554
Number of pages4
ISBN (Print)9781604234497
Publication statusPublished - 2006 Jan 1
EventINTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP - Pittsburgh, PA, United States
Duration: 2006 Sep 172006 Sep 21

Publication series

NameINTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP
Volume3

Conference

ConferenceINTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP
CountryUnited States
CityPittsburgh, PA
Period06/9/1706/9/21

Keywords

  • HLDA
  • MHLDA
  • Robust speech recognition

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Kubo, T., Ogawa, T., & Kobayashi, T. (2006). Manifold HLDA and its application to robust speech recognition. In INTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP (pp. 1551-1554). (INTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP; Vol. 3). International Speech Communication Association.