In this paper we summarise the state-of-the-art for automatic speech recognition in automated Directory Assistance at the start of the 5th Framework project SMADA. Details are given about robust acoustic features for use in Distributed Speech Recognition, especially with respect to noise suppression. Then an overview is given of the confidence measures which are in use today, and their similarities and differences. Finally, work aimed at automatic update of acoustic models and automatic inference of language models is sketched that is becoming possible thanks to the very large amounts of data that can be recorded in operational services.
In addition to summarising the state-of-the-art the paper also indicates the lines along which the research in SMADA will develop.
Cite as: Boves, L., Jouvet, D., Sienel, J., Mori, R.d., Béchet, F., Fissore, L., Laface, P. (2000) ASR for automatic directory assistance: The SMADA project. Proc. ASR2000 - Automatic Speech Recognition: Challenges for the New Millenium, 249-254
@inproceedings{boves00_asr,
author={Lou Boves and Denis Jouvet and Juergen Sienel and Renato de Mori and Fréderic Béchet and Luciano Fissore and Pietro Laface},
title={{ASR for automatic directory assistance: The SMADA project}},
year=2000,
booktitle={Proc. ASR2000 - Automatic Speech Recognition: Challenges for the New Millenium},
pages={249--254}
}