A dynamic programming/neural network approach for connected-speech recognition

摘要

Coarticulation effects and the need to make early decisions require real-time, connected speech recognition systems to use sophisticated final-recognition decision techniques. Attributes such as the ability to form complex decision boundaries in pattern recognition problems make neural networks attractive for performing this final-recognition decision. A combined dynamic programming/neural network approach to connected-speech recognition is evaluated in the context of recognition performance versus a dynamic programming/rule-based expert approach. Discussions of the authors' approach to neural network parameter selection, implementation, and training are included. Results for both the digits and alphadigits vocabularies are given.