Unsupervised Spectral Substraction for Noise-Robust ASR
| Type of publication: | Idiap-RR |
| Citation: | lathoud-rr-05-42 |
| Number: | Idiap-RR-42-2005 |
| Year: | 2005 |
| Institution: | IDIAP |
| Address: | Martigny, Switzerland |
| Note: | Published in Proceedings of the 2005 IEEE ASRU Workshop |
| Abstract: | This paper proposes a simple, computationally efficient \mbox{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 Substraction'' 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. |
| Userfields: | ipdinar={2005}, ipdmembership={speech}, language={English}, |
| Keywords: | |
| Projects: |
Idiap |
| Authors: | |
| Crossref by |
lathoud05e |
| Added by: | [UNK] |
| Total mark: | 0 |
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