Neural networks for speech and sequence recognition
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書誌事項
Neural networks for speech and sequence recognition
International Thomson Computer Press, 1995
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注記
Bibliography: p145-162. - Includes index
内容説明・目次
内容説明
Sequence recognition is a crucial element in many applications in the fields of speech analysis, control and modelling. This text applies the techniques of neural networks and hidden Markov models to the problems of sequence recognition, and as such is intended to prove valuable to researchers and graduate students alike.
目次
- 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.
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