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A comparison-based approach to mispronunciation detection
2012
2012 IEEE Spoken Language Technology Workshop (SLT)
The task of mispronunciation detection for language learning is typically accomplished via automatic speech recognition (ASR). Unfortunately, less than 2% of the world's languages have an ASR capability, and the conventional process of creating an ASR system requires large quantities of expensive, annotated data. In this paper we report on our efforts to develop a comparison-based framework for detecting word-level mispronunciations in nonnative speech. Dynamic time warping (DTW) is carried out
doi:10.1109/slt.2012.6424254
dblp:conf/slt/LeeG12
fatcat:ji25wncfybanpeq5m5ecr27ice