In this paper a speaker adaptation methodology is proposed, which first automatically determines a number of speaker clusters in the training material, then estimates the parameters of the corresponding models, and finally applies a fast match strategy-based on the so called histogram models-to choose the optimal cluster for each test utterance. The fast match strategy is critical to make this methodology useful in real applications, since carrying out several recognition passes-one for each cluster of speakers-, and then selecting the decoded string with the highest likelihood, would be too costly. Preliminary experimentation over two speech databases in Spanish reveal that both the clustering algorithm and the fast match strategy are consistent and reliable. The histogram models, though being suboptimal-they succeeded in guessing the right cluster for unseen test speakers in 85% of the cases with read speech, and in 63% of the cases with spontaneous speech-, yielded around a 6% decrease in error rate in phonetic recognition experiments.
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