Error-responsive feedback mechanisms for speech recognizers
Lin Chase · 1997
This thesis is about modeling, analyzing, and predicting errorful behavior in large vocabulary continuous speech recognition systems. Because today's state-of-the-art recognizers are not designed to be situated naturally in an error feedback loop, they are ill-positioned for inclusion in multi-modal interfaces, multi-media databases, and other interesting applications. I make improvements to the current approach to predicting and analyzing error behaviors, which is currently based only on the measurement of word error rate. The speech recognizer's functionality is extended to include confidence annotations, which are "meta-level" markings that indicate how certain the recognizer is that it has decoded its input correctly. This is accomplished by feeding externally defined error conditions back to the recognizer. Error feedback enables the construction of statistical models that map measurements of the recognizer's internal states and behaviors to externally defined error conditions. ...