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A new approach to minimize utterance verification error rate for a specific operating point

In many telephony applications that use speech recognition, it is important to identify and reject out-of-vocabulary words or utterances without keywords by means of utterance veriﬁcation (UV). Typically, UV is performed based on the likelihood ratio of the target model versus an alternative model. The “goodness” of the models and the particular criteria used for estimating these models can have signiﬁcant impact on its performance. Because the UV problem can be considered as a two-class classiﬁcation problem, minimum classiﬁcation error (MCE) training is a natural choice. Earlier work has focused on MCE training to reduce total classiﬁcation errors. In this paper, we extend the MCE approach to minimize the error rates. In particular, we focus on the error rates at certain operating points and show how this can result in a signiﬁcant EER reduction for phone veriﬁcation on the TIMIT and a non-native kids corpus. While the particular technique is developed on utterance veriﬁcation, it can also be generalized for other veriﬁcation tasks such as speaker veriﬁcation.