Speech Analysis for Automatic Evaluation of Shadowing
نویسندگان
چکیده
This paper presents acoustic analysis for the purpose of automatic evaluation of shadowing speech. We use selfchecked scores of understanding, manual prosodic scores, and TOEIC scores as reference scores of learners’ shadowing speech, and compare these scores with automatic scores based on acoustic features that can reflect phoneme intelligibility and prosodic fluency in terms of intonation, and rhythm. We also examine the differences of personal-best shadowing, shadowing after the transcription is shown and reading-aloud of the same contents. Experimental results show that learners’ understanding of contents in shadowing affects segmental intelligibility and prosodic fluency of their shadowing productions. A multiple regression model that combines different features can better reflect learners’ understanding of the contents of shadowing and other reference scores, and thus suitable for automatic evaluation of shadowing.
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