Improving analysis techniques for automatic speech recognition

Automatic recognition of continuous speech for small vocabularies (e.g., telephone or credit card digit sequences) is possible with excellent accuracy, even in applications using telephone lines and serving a large population of users. However, even such simple recognition tasks suffer decreased performance in adverse conditions, e.g., significant background noise or fading on portable telephone channels. If we further impose significant limitations on the computing resources for the recognition task, then robust efficient speech recognition is still a significant challenge, even for simple vocabularies. Since many practical recognition tasks take place over telephone lines and in conditions that are less than optimal, speech recognition must be robust. The traditional hidden Markov model approach to speech recognition, using cepstral analysis, is computationally intensive and often does not work well under adverse acoustic conditions. We examine a simpler spectral analysis method, and suggest a segmental approach. High recognition accuracy can be maintained, despite a large decrease in both memory and computation, compared to traditional approaches.