Pronunciation Modelling for Conversational Speech Recognition: A Status Report from WS97

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“Pronunciation Modelling for Conversational Speech Recognition: A Status Report from WS97” by W. Byrne, M. Finke, S. Khudanpur, J. Mc{D}onough, H. Nock H, M. Riley, M. Saraclar, C. Wooters, and G. Zavaliagkos. In IEEE Automatic Speech Recognition and Understanding Workshop, 1997.

Abstract

Accurately modelling 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 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 alternate pronunciations in a recognition experiment as well as in the acoustic training of an automatic speech recognition system. Our results show a reduction of word error rate in both cases band 2.2% with acoustic retraining.

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BibTeX entry:

@inproceedings{pmod_asru97,
   author = {W. Byrne and M. Finke and S. Khudanpur and J. Mc{D}onough and
	H. Nock H and M. Riley and M. Saraclar and C. Wooters and G.
	Zavaliagkos},
   title = {Pronunciation Modelling for Conversational Speech Recognition:
	A Status Report from {WS97}},
   booktitle = {{IEEE} Automatic Speech Recognition and Understanding
	Workshop},
   pages = {(8 pages)},
   year = {1997}
}

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