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Conference papers

Statistical Feature Language Model

Kamel Smaïli 1 Salma Jamoussi 1 David Langlois 1 Jean-Paul Haton 1 
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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.
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Submitted on : Tuesday, November 21, 2017 - 11:46:30 PM
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  • HAL Id : inria-00100021, version 1



Kamel Smaïli, Salma Jamoussi, David Langlois, Jean-Paul Haton. Statistical Feature Language Model. 8th International Conference on Spoken Language Processing - ICSLP' 2004, 2004, Jeju, South Korea. 4 p. ⟨inria-00100021⟩



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