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- W2150719436 abstract "A system which combines the good short-time classification properties of the time delay neural network (TDNN) with the good integration and overall recognition capabilities of hidden Markov models (HMMs) is proposed for a speaker-independent speech recognizer. The standard vector quantization is replaced by a TDNN labeler giving phonelike labels. In order to avoid hand segmentation for the training of the TDNN, a separate HMM and a Viterbi alignment derived from it are used. This gives a coarse phonetic segmentation of the training data. >" @default.
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- W2150719436 date "2002-12-04" @default.
- W2150719436 modified "2023-09-24" @default.
- W2150719436 title "TDNN labeling for a HMM recognizer" @default.
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- W2150719436 doi "https://doi.org/10.1109/icassp.1990.115728" @default.
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