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Goals of the chapter

This chapter gives an overview of language modelling in the context of large vocabulary  speech recognition  and covers the following topics:

The primary application we consider is large vocabulary  speech recognition  with applications like text dictation  and automatic dialogue systems.  Some of the techniques presented are maybe useful in other applications, too, like systems for voice commands and guided dialogues, where a finite state network  might be sufficient as language model. For most non-experts and maybe even the experts in speech recognition , it still is a surprise that the trigram  language model performs as well as it does. In contrast, grammar based language models   (i.e. models based on linguistic grammars) are far from being competitive at the present time. Therefore, the description focusses on the trigram  model and related issues such as the sparse data  problem and smoothing.  This chapter is only able to touch upon some of the issues in language modelling. For other overviews, see [Jelinek (1991), Jelinek et al. (1991a), Jelinek et al. (1992)]. For related topics such as the use of stochastic methods for language acquisition and language understanding, see [Gorin et al. (1991)] and [Pieraccini et al. (1993)], respectively.



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