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Computational Models of Speech Pattern Processing pp 304–327Cite as

Using Natural-Language Knowledge Sources in Speech Recognition

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Part of the book series: NATO ASI Series ((NATO ASI F,volume 169))

Summary

High accuracy speech recognition requires a language model, to specify what word sequences are possible or at least likely. Standard n-gram language models for speech recognition ignore linguistic structures, but more linguistically sophisticated language models are possible. Unification grammars are widely used in natural languageand these can be compiled into non-left-recursive context-free grammars that can then be used in realtime speech recognizers by dynamically expanding them into state-transition networks. A hybrid language model incorporating both a unification grammar and n-gram statistics has been shown to increase speech recognition accuracy. Probabilistic context-free grammars and probabilistic unification grammars are also possible.

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Author information

Authors and Affiliations

  1. Microsoft Research One Microsoft Way, Redmond, WA, 98052, USA

    Robert C. Moore

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  1. Robert C. Moore
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Editor information

Editors and Affiliations

  1. Speech Research Unit, DERA Malvern, St. Andrew’s Road, WR14 4DT, Great Malvern, Worcs, UK

    Keith Ponting

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© 1999 Springer-Verlag Berlin Heidelberg

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Moore, R.C. (1999). Using Natural-Language Knowledge Sources in Speech Recognition. In: Ponting, K. (eds) Computational Models of Speech Pattern Processing. NATO ASI Series, vol 169. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60087-6_27

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  • DOI: https://doi.org/10.1007/978-3-642-60087-6_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-64250-0

  • Online ISBN: 978-3-642-60087-6

  • eBook Packages: Springer Book Archive

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