DocumentCode
776442
Title
Data-driven, nonlinear, formant-to-acoustic mapping for ASR
Author
Jackson, P.J.B. ; Lo, B.-H. ; Russell, M.J.
Author_Institution
Dept. of Electron., Electr. & Comput. Eng., Univ. of Birmingham, UK
Volume
38
Issue
13
fYear
2002
fDate
6/20/2002 12:00:00 AM
Firstpage
667
Lastpage
669
Abstract
With a view to using an articulatory representation in automatic recognition of conversational speech, two nonlinear methods for mapping from formants to short-term spectra were investigated: multilayered perceptrons (MLPs), and radial basis function (RBF) networks. Five schemes for dividing the TIMIT data according to their phone class were tested. The r.m.s. error of the RBF networks was 10%, less than that of the MLP, and the scheme based on discrete articulatory regions gave the greatest improvements over a single network
Keywords
multilayer perceptrons; radial basis function networks; speech recognition; ASR; MLP; RBF networks; RMS error; TIMIT data; articulatory representation; automatic speech recognition; conversational speech; formant-to-acoustic mapping; multilayered perceptrons; nonlinear methods; phone class; radial basis function networks; short-term spectra;
fLanguage
English
Journal_Title
Electronics Letters
Publisher
iet
ISSN
0013-5194
Type
jour
DOI
10.1049/el:20020436
Filename
1015750
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