Abstract
Three baseline isolated word rejection strategies are initially proposed and their rejection performance is evaluated on two different types of out-of-vocabulary (OOV) utterances: OOV similar in nature to the in-vocabulary (IV) ones versus OOV consisting of non-speech events such as coughs, clicks, smacks, etc. A general OOV model, referred to as garbage, is added in parallel to the IV models and then a confidence measure, based on the contrast of the first best, hypothesis score with the garbage score, is used as an IV-OOV classifier. The discriminative power of some other confidence measures, utilising the distance between the first two hypotheses in the N-best list, is also investigated. Further, a considerable improvement (up to 34%) of the baseline classification error rate is achieved when the garbage model is supplied with word transition penalties.
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