Discriminant wordspotting techniques for rejecting non-vocabulary utterances in unconstrained speech

Discriminant techniques for training speaker-independent hidden Markov model (HMM) parameters in a wordspotter are proposed for improving the discrimination between keyword utterances and background speech utterances. These techniques are applied in the context of completely unconstrained conversational speech utterances that contain a large number of nonvocabulary words. Hidden Markov keyword model parameters are modified in the procedure so that keyword models are made more probable with respect to a network of background speech models. While there have been many discriminant training techniques proposed for relatively constrained closed vocabulary tasks, the implementation described deals with the more general problem of discriminating keyword utterances from a broad class of acoustic events. Results are presented showing improved wordspotter operating characteristics on a conversational speech utterance