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An HMM-MLP hybrid approach for improving discrimination in speech recognition

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Authors
Na, Kyungmin; Chae, Soo-Ik
Issue Date
1998-05
Citation
Proceedings of the International Joint Conference on Neural Networks, pp.156-159
Abstract
In this paper, we propose an HMMZMLP hybrid scheme
for achieving high discrimination in speech recognition.
Io the conventional hybrid approaches, an MLP is
trained as a distribution estimator or as a VQ labeler,
and the HMMs pegonn recognition using the output of
the MLP, In the proposed method, to the contrary, HMMs
generate a new feature vector of a fixed dimension by
concatenating their state log-likelihoods, and an MLP
discriminator pegoms recognition by using this new
feature vector as an input. The proposed method was
tested on the nine American E-set letters from the
ISOLET database of the OGI. For comparison, a
weighted HMM VHMM) algorithm and GPD-based
WHMM algorithm which use an adaptively-trained linear
discriminator were also tested. In most cases, the
recognition rates on the closed-test and open-test sets of
the proposed method were higher than those of the
conventional methods.
Language
English
URI
http://hdl.handle.net/10371/21380
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Electrical and Computer Engineering (전기·정보공학부)Others_전기·정보공학부
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