This paper presents a new approach to improve the ro-bustness of large vocabulary continuous speech recogni-tion. The proposed technique { based on Singular ValueDecomposition (SVD) { originates from classical signalenhancement, but it is adapted to the specific require-ments imposed by the speech recognition process.Additive noise reduction is obtained by altering the sin-gular value spectrum of the signal observation matrix,thereby preserving speech signal components and sup-pressing noise-related components.The basic algorithms are developed for white noise butthey can easily be extended to the general coloured noisecase. With the aid of a noise reference, non-stationarynoise can be handled as well. All schemes are adaptive,and work in real-time.Recognition experiments on a noise-corrupted databasewith large vocabulary, continuous speech (Resource Man-agement) reveal that relative reductions of the WER1ofmore than 60 % are obtained.
Cite as: Hermus, K., Dologlou, I., Wambacq, P., Compernolle, D.V. (1999) Fully adaptive SVD-based noise removal for robust speech recognition. Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999), 1951-1954, doi: 10.21437/Eurospeech.1999-429
@inproceedings{hermus99_eurospeech,
author={Kris Hermus and Ioannis Dologlou and Patrick Wambacq and Dirk Van Compernolle},
title={{Fully adaptive SVD-based noise removal for robust speech recognition}},
year=1999,
booktitle={Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999)},
pages={1951--1954},
doi={10.21437/Eurospeech.1999-429}
}