Systems for Isolated and Connected Word Recognition

This lecture is intended to provide an insight into some of the algorithms and techniques that lie behind contemporary automatic speech recognition systems. It is noted that, due to the lack of success of earlier phonetically motivated approaches, the majority of current speech recognizers employ whole-word pattern matching techniques. It is pointed out that these techniques, although rather shallow in concept, have enabled the development of commercial recognizers which exhibit useful and practical capabilities. A range of whole-word pattern matching algorithms are discussed, and in particular, key techniques such as dynamic-time-warping and hidden Markov modelling are explained in some detail. It is also shown how techniques for isolated word recognition may be extended to recognize connected speech. Each of the various methods is reviewed in the context of their computational implications as well as their recognition performance. It is also shown how suitable modifications to the basic algorithms can facilitate real-time operation. Where possible, specific techniqes are highlighted by reference to existing commercial recognition equipment. The lecture concludes by focusing on the key factors which limit the performance of current recognition tchniques, and by outlining some of the research work which may be relevant to future automatic speech recognition systems.