| 摘要 |
<p>A method and apparatus for constructing voice templates for a speaker-independent voice recognition system includes segmenting a training utterance to generate time-clustered segments, each segment being represented by a mean. The means for all utterances of a given word are quantized to generate template vectors. Each template vector is compared with testing utterances to generate a comparison result. The comparison is typically a dynamic time warping computation. The training utterances are matched with the template vectors if the comparison result exceeds at least one predefined threshold value, to generate an optimal path result, and the training utterances are partitioned in accordance with the optimal path result. The partitioning is typically a K-means segmentation computation. The partitioned utterances may then be re-quantized and re-compared with the testing utterances until the at least one predefined threshold value is not exceeded.</p> |
