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Context-Dependent Vector Clustering for Speech Recognition

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Automatic Speech and Speaker Recognition

Part of the book series: The Kluwer International Series in Engineering and Computer Science ((SECS,volume 355))

Abstract

The performance of a large vocabulary speech recognition system is critically tied to the quality of the acoustic prototypes that are established in the relevant feature space(s). This is especially true in continuous speech and/or for speaker-independent tasks, where pronunciation variability is the greatest. In this chapter, we will discuss a number of clustering techniques which can be used to derive high quality acoustic prototypes.

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Author information

Authors and Affiliations

  1. Apple Computer, One Infinite Loop, Cupertino, California, 95014, USA

    Jerome R. Bellegarda

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  1. Jerome R. Bellegarda
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Editors and Affiliations

  1. AT&T Bell Laboratories, Murray Hill, NJ, 07974, USA

    Chin-Hui Lee  & Frank K. Soong  & 

  2. School of Microelectronic Engineering, Griffith University, Australia

    Kuldip K. Paliwal

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© 1996 Kluwer Academic Publishers

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Bellegarda, J.R. (1996). Context-Dependent Vector Clustering for Speech Recognition. In: Lee, CH., Soong, F.K., Paliwal, K.K. (eds) Automatic Speech and Speaker Recognition. The Kluwer International Series in Engineering and Computer Science, vol 355. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1367-0_6

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  • DOI: https://doi.org/10.1007/978-1-4613-1367-0_6

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4612-8590-8

  • Online ISBN: 978-1-4613-1367-0

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