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A New Feature Normalization Scheme Based on Eigenspace for Noisy Speech Recognition

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String Processing and Information Retrieval (SPIRE 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3246))

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Abstract

We propose a new feature normalization scheme based on eigenspace, for achieving robust speech recognition. In particular, we employ the Mean and Variance Normalization (MVN) in eigenspace using unique and independent eigenspaces to cepstra, delta and delta-delta cepstra respectively. We also normalize training data in eigenspace and get the model from the normalized training data. 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 obtain a substantial recognition improvement over the basic eigenspace normalization.

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References

  1. Yao, K., Visser, E., Kwon, O., Lee, T.: A Speech Processing Front-End with Eigenspace Normalization for Robust Speech Recognition in Noisy Automobile Environments. In: Eurospeech 2003, pp. 9–12 (2003)

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  2. Molau, S., Keysers, D., Ney, H.: Matching training and test data distributions for robust speech recognition. Speech Communication 41(4), 579–601 (2003)

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  3. Jain, P., Hermansky, H.: Improved Mean and Variance Normalization for Robust Speech Recognition. In: Proc. of ICASSP (2001)

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Author information

Authors and Affiliations

  1. Dept. of Electronics and Computer Engineering, Korea University, Seoul, Korea

    Yoonjae Lee & Hanseok Ko

Authors
  1. Yoonjae Lee
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  2. Hanseok Ko
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Editor information

Editors and Affiliations

  1. Georgia Institute of Technology and Università di Padova,  

    Alberto Apostolico

  2. Department of Information Engineering, University of Padova,  

    Massimo Melucci

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© 2004 Springer-Verlag Berlin Heidelberg

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Lee, Y., Ko, H. (2004). A New Feature Normalization Scheme Based on Eigenspace for Noisy Speech Recognition. In: Apostolico, A., Melucci, M. (eds) String Processing and Information Retrieval. SPIRE 2004. Lecture Notes in Computer Science, vol 3246. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30213-1_11

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  • DOI: https://doi.org/10.1007/978-3-540-30213-1_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23210-0

  • Online ISBN: 978-3-540-30213-1

  • eBook Packages: Springer Book Archive

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