Multi-eigenspace normalization for robust speech recognition in noisy environments

Yoonjae Lee, Hanseok Ko

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

Abstract

In this paper, we propose an effective feature normalization scheme based on eigenspace normalization, for achieving robust speech recognition. In general, Mean and Variance Normalization (MVN) is implemented in cepstral domain. However, another MVN approach using eigenspace was recently introduced, in that the eigenspace normalization procedure performs normalization in a single eigenspace. This procedure consists of linear PCA matrix feature transformation followed by mean and variance normalization of the transformed cepstral feature. In the proposed scheme, we apply independent and unique eigenspaces to cepstra, delta and delta-delta cepstra respectively. We also normalize training data in eigenspace. In addition, a feature space rotation procedure is introduced to reduce the mismatch of training and test data distribution in noisy condition. As a result, we obtained a substantial improvement over the basic eigenspace normalization.

Original languageEnglish
Title of host publication8th International Conference on Spoken Language Processing, ICSLP 2004
PublisherInternational Speech Communication Association
Pages2097-2100
Number of pages4
Publication statusPublished - 2004
Event8th International Conference on Spoken Language Processing, ICSLP 2004 - Jeju, Jeju Island, Korea, Republic of
Duration: 2004 Oct 42004 Oct 8

Other

Other8th International Conference on Spoken Language Processing, ICSLP 2004
CountryKorea, Republic of
CityJeju, Jeju Island
Period04/10/404/10/8

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ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

Cite this

Lee, Y., & Ko, H. (2004). Multi-eigenspace normalization for robust speech recognition in noisy environments. In 8th International Conference on Spoken Language Processing, ICSLP 2004 (pp. 2097-2100). International Speech Communication Association.