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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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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Abstract 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.< >
AbstractList 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.< >
Author Bush, M.A.
Kopec, G.E.
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Snippet The authors present an alternative to the enhancement paradigm for cochannel speech recognition, in which target-interference separation and target recognition...
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StartPage 270
SubjectTerms Automatic speech recognition
Frequency
Gaussian noise
Interference suppression
Linear predictive coding
Noise measurement
Speech enhancement
Speech processing
Speech recognition
Target recognition
Title An LPC-based spectral similarity measure for speech recognition in the presence of co-channel speech interference
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