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Language model adaptation using WFST-based speaking-style translation

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3 Author(s)
Hori, T. ; Speech Open Lab., NTT Commun. Sci. Labs., Kyoto, Japan ; Willett, Daniel ; Minami, Y.

This paper describes a new approach to language model adaptation for speech recognition based on the statistical framework of speech translation. The main idea of this approach is to compose a weighted finite-state transducer (WFST) that translates sentence styles from in-domain to out-of-domain. It enables to integrate language models of different styles of speaking or dialects and even of different vocabularies. The WFST is built by combining in-domain and out-of-domain models through the translation, while each model and the translation itself is expressed as a WFST. We apply this technique to building language models for spontaneous speech recognition using large written-style corpora. We conducted experiments on a 20k-word Japanese spontaneous speech recognition task. With a small in-domain corpus, a 2.9% absolute improvement in word error rate is achieved over the in-domain model.

Published in:

Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on  (Volume:1 )

Date of Conference:

6-10 April 2003