Context dependent vector quantization for continuous speech recognition
The authors present a method for designing a vector quantizer for speech recognition that uses decision networks constructed by examining the phonetic context to obtain models for classes in the quantizer. Diagonal Gaussian models are constructed for the vector quantizer classes at each terminal node of the network and are used to label speech parameter vectors during recognition. Experimental results indicate that this method leads to superior vector quantizers for continuous speech.<<ETX>>