An LPC-based spectral similarity measure for speech recognition in the presence of co-channel speech interference

The authors present an alternative to the enhancement paradigm for cochannel speech recognition, in which target-interference separation and target recognition occur simultaneously, driven by a model of the recognition vocabulary. The method is based on an LPC (linear predictive coding) spectral sim...

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Bibliographic Details
Published inInternational Conference on Acoustics, Speech, and Signal Processing pp. 270 - 273 vol.1
Main Authors Kopec, G.E., Bush, M.A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1989
Subjects
Automatic speech recognition
Frequency
Gaussian noise
Interference suppression
Linear predictive coding
Noise measurement
Speech enhancement
Speech processing
Speech recognition
Target recognition
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Summary:The authors present an alternative to the enhancement paradigm for cochannel speech recognition, in which target-interference separation and target recognition occur simultaneously, driven by a model of the recognition vocabulary. The method is based on an LPC (linear predictive coding) spectral similarity measure which allows a reference spectrum to match only a subset of the poles of a noisy input spectrum, rather than requiring a whole-spectrum comparison. A preliminary evaluation of the proposed method in a speaker-trained isolated-digit recognition task suggests a reduction in error rate of 50-70% at low target-interference ratios, as compared to a conventional whole-spectrum similarity measure.< >
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.1989.266417