Exploiting variable-width features in large vocabulary speech recognition

A framework for the use of variable-width features is presented which employs the N-best algorithm with the features being applied in a postprocessing phase. The framework is flexible and widely applicable, giving greater scope for exploitation of the features than previous approaches. large-vocabulary speech recognition experiments using TIMIT show that the application of variable-width features has potential benefits. The lack of robustness in some past schemes can be overcome by virtue of the scoring flexibility inherent in the proposed scheme and the use of front-end recognizer output to assist the feature extraction process. The framework also has the advantage of not being tied to a specific front-end recognizer architecture. The method presented allows the features to be used in new ways with, for instance, the availability of complete utterance transcriptions providing a useful additional source of information.<>