Unsupervised Spectral Subtraction for Noise-Robust ASR
This paper proposes a simple, computationally efficient 2-mixture model approach to discriminate between speech and background noise at the magnitude spectrogram level. It is directly derived from observations on real data, and can be used in a fully unsupervised manner, with the EM algorithm. In this paper, the 2-mixture model is used in an ``Unsupervised Spectral Subtraction'' scheme that can be applied as a pre-processing step for any acoustic feature extraction scheme, such as MFCCs or PLP. The goal is to improve noise-robustness of the acoustic features. Experimental results on both OGI Numbers 95 and Aurora 2 tasks yielded a major improvement on all noise conditions, while retaining a similar performance on clean conditions.
- URL: http://publications.idiap.ch/downloads/papers/2005/lathoud05e.pdf
- Related documents: http://publications.idiap.ch/index.php/publications/showcite/lathoud-rr-05-42
IDIAP RR 05-42
Record created on 2010-02-11, modified on 2016-08-08