A generalization of isolated word recognition using vector quantization

The use of vector quantization (VQ) for isolated-word speech recognition of a 20-word vocabulary was shown in previous work to achieve more than 99% accuracy for speaker-dependent recognition and 87% accuracy for speaker-independent recognition. Separate VQ codebooks were designed for each word in the recognition vocabulary, and input words were classified by performing VQ and finding the codebook that achieves the smallest average distortion. The method obviates time-normalization and makes no use of time-sequence information. This paper presents results for a generalization that incorporates time-sequence information. The generalization, which was motivated by work of Martinez, Riveria, and Buzo, is more accurate and faster than the previous method. Words in the training and input sequences are normalized linearly to the same length and then divided into sections. Separate VQ codebooks are designed for each section of each vocabulary word. Each vocabulary word is then represented by a "multisection" codebook - a time-dependent sequence of section-codebooks. New words are classified by performing VQ and finding the multi-section codebook that achieves the smallest average distortion. Initial tests on a twenty-word vocabulary resulted in accuracies greater than 97% for speaker-independent recognition.