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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.
List of Figures. List of Tables. Notation. Foreword. Preface. I: Background. 1. Introduction. 2. Statistical Pattern Classification. 3. Hidden Markov Models. 4. Multilayer Perceptions. II: Hybrid HMM/MLP Systems. 5. Speech Recognition using ANNs. 6. Statistical Inference in MLPs. 7. The Hybrid HMM/MLP Approach. 8. Experimental Systems. 9. Context-Dependent MPLs. 10. System Tradeoffs. 11. Training Hardware and Software. III: Additional Topics. 12. Cross-Validation in MLP Training. 13. HMM/MLP and Predictive Models. 14. Feature Extraction by MLP. IV: Finale. 15. Final System Overview. 16. Conclusions. Bibliography. Index. Acronyms.