Abstract
Utterance verification represents an important technology in the design of user-friendly speech recognition systems. This paper addresses the issue of robustness in utterance verification. Four different approaches to robustness have been investigated: a string based likelihood measure for the detection of non-vocabulary words and putative errors, a signal bias removal method for channel normalization, on-line adaptation technique for achieving desirable trade-off between false rejection and false alarms, and a discriminative training method for the minimization of the expected string error rate. When these techniques were all integrated into a state-of-the-art connected digit recognition system, the string error rate was found to decrease by up to 57% at a rejection rate of 5%. For non-vocabulary word strings, the proposed utterance verification system rejected over 99.9% of extraneous speech.
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