A Hybrid Approach to Improving Automatic Speech Recognition Via NLP

Authors:
Kimberly Voll
Affiliations:
School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A1S6,
Venue:
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Year:
2007

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Abstract

In many domains, automated speech recognition (ASR) demands highly robust and accurate recognition software. Unfortunately, in such domains, even a 99% accurate recognizer is inadequate, and other methods for increasing the reliability and performance of ASR must be considered. As a possible solution to this problem, post-speech-recognition error detection can assist in proofreading more efficiently. To this end, we have developed a multi-heuristic algorithm using natural language processing to detect recognition errors. As a proof of concept, we have applied this algorithm to the radiology domain. The results are encouraging, showing a 22% increase in the recall performance, and a 6% increase in the precision performance, over the best individual technique.