New MAP estimators for speaker recognition
نویسندگان
چکیده
We report the results of some experiments which demonstrate that eigenvoice MAP and eigenphone MAP are at least as effective as classical MAP for discriminative speaker modeling on SWITCHBOARD data. We show how eigenvoice MAP can be modified to yield a new model-based channel compensation technique which we call eigenchannel MAP. When compared with multi-channel training, eigenchannel MAP was found to reduce speaker identification errors by 50%.
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