Handbook of Neural Networks for Speech Processing
Artech House, 2000 - Computers - 522 pages
Here are the comprehensive details on cutting edge technologies employing neural networks for speech recognition and speech processing in modern communications. Going far beyond the simple speech recognition technologies on the market today, this new book, written by and for speech and signal processing engineers in industry, R&D, and academia, takes you to the forefront of the hottest emergent neural net-based speech processing techniques.
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acoustic feature vector algorithm applications approach architecture Bayes decision cepstrum characteristics classification error codebook coder computation Conf connectionist continuous speech corresponding defined design samples discriminant function discriminative training distance dynamic estimation evaluated feature extraction Figure filter formant frame frequency FSVQ Gaussian Hidden Markov Models hybrid IEEE IEEE Int IEEE Trans Katagiri layer learning linear prediction loss function mapping method minimization minimum misclassification measure neural network neurons node noise nonlinear predictive optimal output parameters Parzen window pattern recognition performance phoneme posterior probabilities probabilistic problem Proc prototype prototype-based recognizer recurrent neural network Section segments sequence Signal Processing source speaker speaker recognition speaker verification spectral speech coding speech processing speech production speech recognition speech signal suprasegmental target speaker task TDNN techniques tion training data training set transformation vector quantization vocal folds vocal tract voice conversion vowel window word