Automatic Detection of Word-Level Reading Errors in Non-native English Speech Based on ASR Output

Ying Qin, Yao Qian, Anastassia Loukina, Patrick L. Lange, Abhinav Misra, Keelan Evanini, Tan Lee

Abstract: Automated reading error detection has attracted a lot of interest in the area of computer-assisted language learning and auto-mated reading tutors. This paper presents preliminary experimental results on automatic detection of word-level reading errors in non-native speech. A state-of-the-art large vocabulary automatic speech recognition (ASR) system is developed to transcribe non-native speech, with performance comparable to humans in transcribing non-native read speech data. With this ASR system, we investigate the feasibility of detecting substitution, insertion and deletion errors from ASR decoding results on non-native read speech. Experimental results show that the performance of detecting substitution and insertion errors are on the low side. Several possible reasons for causing such results are discussed in this paper. Common types of reading errors occurring in non-native read speech and those that are difficult to be detected are analyzed for future investigation.