Data-driven, nonlinear, formant-to-acoustic mapping for ASR

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Data-driven, nonlinear, formant-to-acoustic mapping for ASR

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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.

Inspec keywords: speech recognition; radial basis function networks; multilayer perceptrons

Other keywords: TIMIT data; RBF networks; nonlinear methods; RMS error; formant-to-acoustic mapping; radial basis function networks; automatic speech recognition; short-term spectra; ASR; phone class; multilayered perceptrons; articulatory representation; MLP; conversational speech

Subjects: Speech recognition; Neural computing techniques; Speech recognition and synthesis

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      • Richards, H.B., Bridle, J.S.: `The HDM: a segmental hidden dynamic model of coarticulation', Proc. IEEE-ICASSP'99, 1999, p. 357–360.
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      • Holmes, J.N.J.: `Speech processing system using formant analysis', US patent, 6292775, September 2001.
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      • W.J. Holmes , M.J. Russell . Probabilistic-trajectory segmental HMMs. Comput. Speech Lang. , 1 , 3 - 37
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      • Holmes, W.J., Holmes, J.N., Garner, P.N.: `Using formant frequencies in speech recognition', Proc. Eurospeech'97, 1997, p. 2083–2086.
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