Neural networks for speech and sequence recognition
Connectionist models Learning theory The back-propagation algorithm Introduction to back-propagation Formal description Heuristics to improve convergence and generalization Extensions Integrating domain knowledge and learning from examples Automatic speech recognition Importance of pre-processing input data Input coding. Input invariances Importance of architecture constraints on the network Modularization Output coding Sequence analysis Introduction Time delay neural networks Recurrent networks BPS Supervision of a recurrent network does not need to be everywhere Problems with training of recurrent networks Dynamic programming post-processors Hidden Markov models Integrating ANNs with other systems Advantages and disadvantages of current algorithms for ANNs Modularization and joint optimization Radial basis functions and local representation Radial basis funtions networks Neurobiological plausibility Relation to vector quantization, clustering and semi-continuous HMMs Methodology Experiments on phoneme recognition with RBFs Density estimation with a neural network Relation between input PDF and output PDF Density estimation Conclusion Post-processors based on dynamic programming ANN/DP hybrids ANN/HMM Hybrids ANN/HMM Hybrid: Phoneme recognition experiments ANN/HMM hybrid: online handwriting recognition experiments.