Connectionist Speech Recognition: A Hybrid Approach
Connectionist Speech Recognition: A Hybrid Approach describes the theory and implementation of a method to incorporate neural network approaches into state of the art continuous speech recognition systems based on hidden Markov models (HMMs) to improve their performance. In this framework, neural networks (and in particular, multilayer perceptrons or MLPs) have been restricted to well-defined subtasks of the whole system, i.e. HMM emission probability estimation and feature extraction.
The book describes a successful five-year international collaboration between the authors. The lessons learned form a case study that demonstrates how hybrid systems can be developed to combine neural networks with more traditional statistical approaches. The book illustrates both the advantages and limitations of neural networks in the framework of a statistical systems.
Using standard databases and comparison with some conventional approaches, it is shown that MLP probability estimation can improve recognition performance. Other approaches are discussed, though there is no such unequivocal experimental result for these methods.
Connectionist Speech Recognition is of use to anyone intending to use neural networks for speech recognition or within the framework provided by an existing successful statistical approach. This includes research and development groups working in the field of speech recognition, both with standard and neural network approaches, as well as other pattern recognition and/or neural network researchers. The book is also suitable as a text for advanced courses on neural networks or speech processing.
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STATISTICAL PATTERN CLASSIFICATION
HIDDEN MARKOV MODELS
SPEECH RECOGNITION USING ANNs
STATISTICAL INFERENCE IN MLPs
THE HYBRID HMMMLP APPROACH
TRAINING HARDWARE AND SOFTWARE
CROSSVALIDATION IN MLP TRAINING
HMMMLP AND PREDICTIVE MODELS
FEATURE EXTRACTION BY MLP
FINAL SYSTEM OVERVIEW
acoustic vectors ANNs associated assumed assumptions autoregressive Bayes Bourlard & Wellekens cepstral Chapter class qk computation connectionist constraints context-dependent contextual inputs continuous speech recognition covariance covariance matrix cross-validation database described discriminant functions dynamic programming emission probabilities error feature vector frame level Gaussian given gradient hidden layer Hidden Markov Model hidden units hybrid HMM/MLP approach improve input features input field input pattern input vector iterations MAP probabilities matrix Maximum Likelihood minimization MLP outputs MLP training Multilayer Perceptron neural networks number of parameters observed optimal output layer output units output values pattern classification perceptron performance phone models phoneme possible posterior probabilities prior probabilities probability density functions problem prototype vector RBFs recurrent Section segmentation sentences shown sigmoid function speaker speaker-independent speech units standard HMMs statistical techniques test set tion topology training data training patterns training set transition probabilities triphone Viterbi algorithm Viterbi search weights word recognition