A two-level schema for detecting recognition errors
Zhengyu Zhou, Helen M. L. Meng · 2004
This paper proposes a two-level schema for the automatic detection of possible errors in speech recognition hypotheses. Given the recognition hypothesis of an utterance, the first level in our schema applies an utterance classifier (UC) to decide if the hypothesis is error-free or erroneous. In the latter case, the utterance is passed on to the second level in our schema for further processing. A word classifier (WC) is applied to each of the word hypotheses in the utterance to decide whether or not it is a misrecognition. Hence the two-level schema can locate error-containing regions in the recognition hypotheses. These are the target regions to which we can apply more sophisticated and expensive language models for error correction as a next step. We have developed UC and WC based on Support Vector Machines (SVM). Experiments on Mandarin Chinese speech recognition using the Speech-Lab-In-A-Box corpora showed that the UC has a detection error rate of 16.5 % for misrecognized utterances; the WC has a detection error rate of 19.8 % for erroneous word hypotheses; and the overall two-level schema can catch 44.5 % of the erroneous word hypotheses. 1.