DocumentCode
1544832
Title
Reducing the effects of linear channel distortion on continuous speech recognition
Author
Bates, R.A. ; Ostendorf, M.
Author_Institution
Dept. of Electr. Comput. & Syst. Eng., Boston Univ., MA, USA
Volume
7
Issue
5
fYear
1999
fDate
9/1/1999 12:00:00 AM
Firstpage
594
Lastpage
597
Abstract
Linear channel compensation in speech recognition typically involves estimating an additive shift in the cepstral domain. This paper explores both Bayesian and maximum likelihood techniques to transform either the features or the model parameters. Experiments on the Macrophone corpus show error rate reductions of up to 16% over cepstral mean subtraction for short utterances
Keywords
Bayes methods; cepstral analysis; maximum likelihood estimation; speech recognition; telecommunication channels; Bayesian technique; Macrophone corpus; additive shift estimation; cepstral domain; cepstral mean subtraction; continuous speech recognition; error rate reductions; experiments; linear channel compensation; linear channel distortion; maximum likelihood technique; model parameters; short utterances; Cepstral analysis; Channel estimation; Collision mitigation; Distortion; Hidden Markov models; Maximum likelihood estimation; Signal to noise ratio; Speech recognition; Telephony; Vectors;
fLanguage
English
Journal_Title
Speech and Audio Processing, IEEE Transactions on
Publisher
ieee
ISSN
1063-6676
Type
jour
DOI
10.1109/89.784112
Filename
784112
Link To Document