Recognition rate estimation based on word alignment network and discriminative error type classification

Abstract

Techniques for estimating recognition rates without using reference transcriptions are essential if we are to judge whether or not speech recognition technology is applicable to a new task. This paper proposes two recognition rate estimation methods for continuous speech recognition. The first is an easy-to-use method based on a word alignment network (WAN) obtained from a word confusion network through simple conversion procedures. A WAN contains the correct (C), substitution error (S), insertion error (I) and deletion error (D) probabilities word-by-word for a recognition result. By summing these CSID probabilities individually, the percent correct and word accuracy (WACC) can be estimated without using a reference transcription. The second more advanced method refines the CSID probabilities provided by a WAN based on discriminative error type classification (ETC) and estimates the recognition rates more accurately. In the experiments on the MIT lecture speech corpus, we obtained 0.97 of correlation coefficient between the true WACCs calculated by a scoring tool using reference transcriptions and the WACCs estimated from the discriminative ETC results.