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Please use this identifier to cite or link to this item: http://hdl.handle.net/1842/1114

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Title: IPA: improved phone modelling with recurrent neural networks
Authors: Robinson, Tony
Hochberg, Mike
Renals, Steve
Issue Date: Apr-1994
Citation: Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on, Volume i, 19-22 April 1994 Page(s):I/37 - I/40.
Publisher: IEEE
Abstract: This paper describes phone modelling improvements to the hybrid connectionist-hidden Markov model speech recognition system developed at Cambridge University. These improvements are applied to phone recognition from the TIMIT task and word recognition from the Wall Street Journal (WSJ) task. A recurrent net is used to map acoustic vectors to posterior probabilities of phone classes. The maximum likelihood phone or word string is then extracted using Markov models. The paper describes three improvements: connectionist model merging; explicit presentation of acoustic context; and improved duration modelling. The first is shown to provide a significant improvement in the TIMIT phone recognition rate and all three provide an improvement in the WSJ word recognition rate.
URI: http://ieeexplore.ieee.org/servlet/opac?punumber=3104
http://hdl.handle.net/1842/1114
ISSN: 1520-6149
Appears in Collections:CSTR publications

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