Sixth European Conference on Speech Communication and Technology

Budapest, Hungary
September 5-9, 1999

A Comparison of Two Strategies for ASR in Additive Noise: Missing Data and Spectral Subtraction

Christopher Kermorvant, Andrew Morris

IDIAP, Martigny, Switzerland.

This paper addresses the problem of speech recognition in the presence of additive noise. To deal with this problem, it is possible to estimate the noise characteristics using methods which have previously been developed for speech enhancement techniques. Spectral subtraction can then be used to reduce the effect of additive noise on speech in the spectral domain. Some techniques have also recently been proposed for recognition with missing data. These approaches require an estimation of the local SNR to detect the speech spectral features which are relatively free from noise so as to perform recognition on these parts only. In this article, we compare these two different strategies, spectral subtraction and "missing data", on continuous speech additively disturbed with real noise. It is shown that missing data methods can improve recognition performance under certain noise conditions but still need to be improved in order to to reach the performance of the spectral subtraction.

Full Paper (PDF)

Bibliographic reference.  Kermorvant, Christopher / Morris, Andrew (1999): "A comparison of two strategies for ASR in additive noise: missing data and spectral subtraction", In EUROSPEECH'99, 2841-2844.