%Aigaion2 BibTeX export from Idiap Publications
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@TECHREPORT{lathoud-rr-05-42,
         author = {Lathoud, Guillaume and Magimai.-Doss, Mathew and Mesot, Bertrand and Bourlard, Herv{\'{e}}},
       projects = {Idiap},
          title = {Unsupervised {S}pectral {S}ubstraction for Noise-{R}obust {ASR}},
           type = {Idiap-RR},
         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.},
            pdf = {https://publications.idiap.ch/attachments/reports/2005/rr-05-42.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2005/rr-05-42.ps.gz},
ipdinar={2005},
ipdmembership={speech},
language={English},
}