Abstract
A hybrid approach to speaker-dependent isolated word recognition is discussed. The approach merges speaker-specific information obtained from a single training utterance with multisection vector quantization codebooks that were designed for speaker-independent recognition. The approach provides easily trained, computationally efficient, and accurate isolated word recognition. On the digits, the approach achieved an error rate less than 1 percent.
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