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Title: Connectionist probability estimation in the DECIPHER speech recognition system
Authors: Renals, Steve
Morgan, Nelson
Cohen, Michael
Franco, Horacio
Issue Date: Mar-1992
Citation: Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on, Volume 1, 23-26 March 1992 Page(s):601 - 604.
Publisher: IEEE
Abstract: The authors have previously demonstrated that feedforward networks can be used to estimate local output probabilities in hidden Markov model (HMM) speech recognition systems (Renals et al., 1991). These connectionist techniques are integrated into the DECIPHER system, with experiments being performed using the speaker-independent DARPA RM database. The results indicate that: connectionist probability estimation can improve performance of a context-independent maximum-likelihood-trained HMM system; performance of the connectionist system is close to what can be achieved using (context-dependent) HMM systems of much higher complexity; and mixing connectionist and maximum-likelihood estimates can improve the performance of the state-of-the-art context-independent HMM system.
URI: Digital Object Identifier 10.1109/ICASSP.1992.225837
ISSN: 1520-6149
Appears in Collections:CSTR publications

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