Adapting grapheme-to-phoneme conversion for name recognition
- Xiao Li ,
- Alex Acero ,
- Asela Gunawardana
IEEE Workshop on Automatic Speech Recognition and Understanding |
Published by Institute of Electrical and Electronics Engineers, Inc.
This work investigates the use of acoustic data to improve grapheme-to-phoneme conversion for name recognition. We introduce a joint model of acoustics and graphonemes, and present two approaches, maximum likelihood training and discriminative training, in adapting graphoneme model parameters. Experiments on a large-scale voice-dialing system show that the maximum likelihood approach yields a relative 7% reduction in SER compared to the best baseline result we obtained without leveraging acoustic data, while discriminative training enlarges the SER reduction to 12%.
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