We formulate a training framework and present a method for task independent utterance verification. Verification-specific HMMs are defined and discriminatively trained using minimum verification error training. Task independence is accomplished by performing the verification on the subword level and training the verification models using a general phonetically balanced database that is independent of the application tasks. Experimental results show that the proposed method significantly outperforms two other commonly used task independent utterance verification techniques. It is shown that the equal error rate of false alarms and false keyword rejection is reduced by more than 22% compared to the other two methods on a large vocabulary recognition task.
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