Pronunciation modelling for conversational speech recognition: a status report from WS97

Accurately modelling of pronunciation variability in conversational speech is an important component for automatic speech recognition. We describe some of the projects undertaken in this direction at WS97 [the Fifth LVCSR (large-vocabulary conversational speech recognition) Summer Workshop], held at Johns Hopkins University, Baltimore, in July-August 1997. We first illustrate a use of hand-labelled phonetic transcriptions of a portion of the Switchboard corpus, in conjunction with statistical techniques, to learn alternatives to canonical pronunciations of words. We then describe the use of these alternative pronunciations in a recognition experiment as well as in the acoustic training of an automatic speech recognition system. Our results show a reduction of the word error rate in both cases-0.9% without acoustic retraining and 2.2% with acoustic retraining.