Improving basic methods of automatic speech recognition

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The biggest problem in automatic speech recognition (ASR) is robustness - making sure that ASR models are general enough. A robust ASR system should be able to decode speech from any speaker, in any reasonable environment. In practice, speakers other than those that trained the ASR models lead to a significant decrease in ASR accuracy. As well, environmental noise (e.g., weather, other talkers, cars) and communication distortions (e.g., with mobile phones) degrade ASR performance, often severely. Even simple microphone substitution reduces accuracy. Human listeners, in contrast, often can adapt rapidly to all these difficulties, which strongly suggests that major flaws exist in current ASR schemes. While it is not necessary to directly mimic human perception when designing ASR systems, much of what we know about human speech production and perception has yet to be integrated into practical ASR. ASR can be greatly improved by better integration of structural information with the current stochastic methodology.