Statistical Feature Language Model
Identifieur interne : 006D33 ( Main/Exploration ); précédent : 006D32; suivant : 006D34Statistical Feature Language Model
Auteurs : Kamel Smaïli [France] ; Salma Jamoussi [France] ; David Langlois [France] ; Jean-Paul Haton [France]Source :
Descripteurs français
- mix :
Abstract
Statistical language models are widely used in automatic speech recognition in order to constrain the decoding of a sentence. Most of these models derive from the classical n-gram paradigm. However, the production of a word dends on a large set of linguistic features : lexical, syntactic, semantic, etc. Moreover, in some natural languages the gender and number of the left context affect the production of the next word. Therefore, it seems attractive to design a language model based on a variety of word features. We present in this paper a new statistical language model, called Statistical Feature Language Model, SFLM, based on this idea. In SFLM a word is considered as an array of linguistic features, and the model is defined in a way similar to the n-gram model. Experiments carried out for French and show an improvement in terms of perplexity and predicted words.
Url:
Affiliations:
- France
- Grand Est, Lorraine (région)
- Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Lorraine
Links toward previous steps (curation, corpus...)
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Le document en format XML
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<profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>automatic speech recognition</term>
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<front><div type="abstract" xml:lang="en">Statistical language models are widely used in automatic speech recognition in order to constrain the decoding of a sentence. Most of these models derive from the classical n-gram paradigm. However, the production of a word dends on a large set of linguistic features : lexical, syntactic, semantic, etc. Moreover, in some natural languages the gender and number of the left context affect the production of the next word. Therefore, it seems attractive to design a language model based on a variety of word features. We present in this paper a new statistical language model, called Statistical Feature Language Model, SFLM, based on this idea. In SFLM a word is considered as an array of linguistic features, and the model is defined in a way similar to the n-gram model. Experiments carried out for French and show an improvement in terms of perplexity and predicted words.</div>
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