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 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.<<ETX>>

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