Constraint selection for topic-based MDI adaptation of language models

Gwénolé Lecorvé 1 Guillaume Gravier 1 Pascale Sébillot 1
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : Constraint selection for topic-based MDI adaptation of language models This paper presents an unsupervised topic-based language model adaptation method which specializes the standard minimum information discrimination approach by identifying and combining topic-specific features. By acquiring a topic terminology from a thematically coherent corpus, language model adaptation is restrained to the sole probability re-estimation of n-grams ending with some topic-specific words, keeping other probabilities untouched. Experiments are carried out on a large set of spoken documents about various topics. Results show significant perplexity and recognition improvements which outperform results of classical adaptation techniques.
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Gwénolé Lecorvé, Guillaume Gravier, Pascale Sébillot. Constraint selection for topic-based MDI adaptation of language models. 10th Annual Conference of the International Speech Communication Association, Interspeech'09, Sep 2009, Brighton, United Kingdom. pp.368--371. ⟨hal-00760610⟩

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