Automated training for speech recognition

Abstract

A method is described for automated training of a speaker-independent isolated word recognizer. The training process generates vocabulary templates from a database of collected training utterances. These templates are then modified through adaptive training, an iterative process of testing and modifying templates in order to optimize recognition. Robustness of the templates is enhanced by varying the presentation of the collected utterances during adaptation; varying the utterance sampling rate, for example, has the effect of presenting the same utterance at differing pitches and time scales. Adaptive training continues until the error rate falls to an acceptable level. Results will bc presented for similar vocabularies developed with and without adaptation and under varying adaptation conditions.