Missing data speech recognition in reverberant conditions

In this study we describe an auditory processing front-end for missing data speech recognition, which is robust in the presence of reverberation. The model attempts to identify time-frequency regions that are not badly contaminated by reverberation and have strong speech energy. This is achieved by...

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Bibliographic Details
Main Authors: Palomaki, Kalle J., Brown, Guy J., Barker, Jon
Format: Conference Proceeding
Language:English
Subjects:
Biological system modeling
Character recognition
Hidden Markov models
Pollution measurement
Reverberation
Speech
Speech recognition
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Description
Summary:In this study we describe an auditory processing front-end for missing data speech recognition, which is robust in the presence of reverberation. The model attempts to identify time-frequency regions that are not badly contaminated by reverberation and have strong speech energy. This is achieved by applying reverberation masking. Subsequently, reliable time-frequency regions are passed to a 'missing data' speech recogniser for classification. We demonstrate that the model improves recognition performance in three different virtual rooms where reverberation time T60 varies from 0.7 sec to 2.7 sec. We also discuss the advantages of our approach over RASTA and modulation filtered spectrograms.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2002.5743655