Confidence scoring and rejection using multi-pass speech recognition
نویسنده
چکیده
This paper presents a computationally efficient method for using multiple speech recognizers in a multi-pass framework to improve the rejection performance of an automatic speech recognition system. A set of criteria is proposed, which determine at run time when rescoring using a second pass is expected to improve the rejection performance. The second pass result is used along with a set of features derived from the first pass to compute a combined confidence score. The feature combination is optimized globally based on training data. The combined system significantly outperforms a simple two-pass system at little more computational cost than comparable one-pass and twopass systems.
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