Abstract : This paper focuses on language model adaptation, and more especially on topic identification (TID) for Automatic Speech Recognition (ASR). The structure of a set of topics is redefined by the introduction of a hierarchy. TID models may then make use of the semantic relationships between parent and son nodes of the topic-tree. The originality of the approach presented in this article lies in the allocation of a unique vocabulary to brother nodes, which rests on the use of two backing-off levels. In comparison with TID performance when using a non-hierarchical approach, results encourage us to carry on in this way.
https://hal.inria.fr/inria-00107536
Contributeur : Publications Loria
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Soumis le : jeudi 19 octobre 2006 - 09:00:24
Dernière modification le : jeudi 11 janvier 2018 - 06:19:55
Document(s) archivé(s) le : vendredi 25 novembre 2016 - 13:04:41
Brigitte Bigi, Armelle Brun, Kamel Smaïli, Jean-Paul Haton. A Hierarchical Approach for Topic Identification. Proceedings of the international workshop Speech and Computer - SPECOM'01, Nov 2001, Moscow, Russia, France. 4 p, 2001. 〈inria-00107536〉
