Connectionist Speech Recognition

Connectionist Speech Recognition

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.

Connectionist Speech Recognition: Status and Prospects

Abstract: "We report on recent advances in the ICSI connectionist speech recognition project. Highlights include: Experimental results showing that connectionist methods can improve the performance of a context independent maximum likelihood trained HMM system, resulting in a performance close to that achieved using state of the art context dependent HMM systems of much higher complexity. Mixing (context independent) connectionist probability estimates with maximum likelihood trained context dependent models to improve the performance of a state of the art system. The development of a network decomposition method that allows connectionist modelling of context dependent phones efficiently and parsimoniously, with no statistical independence assumptions."

Data Selection and Model Combination in Connectionist Speech Recognition