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Phoneme recognition with statistical modeling of the prediction error of neural networks
This paper presents a speech recognition system which
incorporates predictive neural networks. The neural networks
are used to predict observation vectors of speech. The prediction
error vectors are modeled on the state level by Gaussian
densities, which provide the local similarity measure for the
Viterbi algorithm during recognition. The system is evaluated on
a continuous speech phoneme recognition task. Compared with a
HMM reference system, the proposed system obtained better
results in the speech recognition experiments.Peer ReviewedPostprint (published version
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