Pronounciation analysis and mispronounciation detection

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2022

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Abstract

We are interested in developing software that can automatically recognize particular phone segments that a non-native student of a foreign language has pronounced incorrectly. A language training system can provide the student with feedback about individual pronunciation errors by using the information about the phone level. For this purpose, in this work, I am trying to develop a transformer model that will recognize spell errors in speech. There were two strategies that were examined: the first one on the original audio dataset, and the second one on the synthetically augmented dataset. Both experiments were compared in this work.

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software, phone, segments, a non-native student, foreign language, dataset

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