Abstract
We describe an algorithm based on acoustic clustering and acoustic adaptation to significantly improve speech recognition performance. The method is particularly useful when speech from multiple speakers is to be recognized and the boundary between speakers is not known. We assume that each test data segment is relatively homogeneous with respect to the acoustic background and speaker. These segments are then grouped using an agglomerative acoustic clustering algorithm. The idea is to group together all test segments that are acoustically similar. The speech recognition models are then adapted separately to each test data cluster. Finally these adapted models are used to recognize the data from that cluster.
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