Vocabulary independent discriminative utterance verification for nonkeyword rejection in subword based speech recognition

An integral part of any deployable speech recognition system is the capability to detect if the input speech does not contain any of the words in the recognizer vocabulary set. This capability, which is called utterance verification (or keyword recognition and nonkeyword rejection), is therefore becoming increasingly important as speech recognition systems continue to migrate from the laboratory to actual applications. We present a framework and a method for vocabulary independent utterance verification in subword-based speech recognition. The verification process is cast as a statistical hypothesis test, where vocabulary independence is accomplished through a two-stage verification process: subword-level verification followed by string-level verification. A verification function is defined and discriminatively trained to perform subword-level verification. String-level verification is accomplished by defining and evaluating an overall string-level log likelihood ratio that is a function of the subword-level verification scores. Experimental results show that this vocabulary-independent discriminative utterance verification method significantly outperforms a baseline method commonly used in wordspotting tasks.