More Accurate and Efficient Analysis for Automatic Speech Recognition

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Efficient communicating with machines via voice will facilitate interactions that so far have been hindered by awkward interfaces such as keyboards and telephone keypads. People nowadays have increasing need to interact with computers, yet this is still done mostly by typing. Even attempts to seek information by telephone often require many cycles of listening to long messages and then pushing a button, because our capability to do reliable automatic speech recognition (ASR) is quite limited. For some time now, refinements to basic ASR methods established years ago have improved performance, without radical changes to the basic approaches. The recent vast increase in computational power and memory size has led researchers to attack increasingly difficult tasks such as continuously-spoken, very-large-vocabulary, speaker-independent, noisy speech over the telephone. For some limited tasks, e.g., recognizing credit card numbers, or words drawn from medium-sized vocabularies and spoken with frequent pauses, recognition accuracy rises above 99%, and hence practical commercial products are available. However, progress has been slow for the more difficult tasks of recognizing conversational speech or noisy speech. Furthermore, recognition error rates remain high when generalized speaker-independent models are used to decode speakers not used in the training phase.