Automatic proficiency assessment of Korean speech read aloud by non-natives using bidirectional LSTM-based speech recognition

This paper presents an automatic proficiency assessment method for a non-native Korean read utterance using bidirectional long short-term memory (BLSTM)-based acoustic models (AMs) and speech data augmentation techniques. Specifically, the proposed method considers two scenarios, with and without pr...

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Veröffentlicht in:ETRI journal 2020-10, Vol.42 (5), p.761-772
Hauptverfasser: Oh, Yoo Rhee, Park, Kiyoung, Jeon, Hyung-Bae, Park, Jeon Gue
Format: Artikel
Sprache:kor
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Zusammenfassung:This paper presents an automatic proficiency assessment method for a non-native Korean read utterance using bidirectional long short-term memory (BLSTM)-based acoustic models (AMs) and speech data augmentation techniques. Specifically, the proposed method considers two scenarios, with and without prompted text. The proposed method with the prompted text performs (a) a speech feature extraction step, (b) a forced-alignment step using a native AM and non-native AM, and (c) a linear regression-based proficiency scoring step for the five proficiency scores. Meanwhile, the proposed method without the prompted text additionally performs Korean speech recognition and a subword un-segmentation for the missing text. The experimental results indicate that the proposed method with prompted text improves the performance for all scores when compared to a method employing conventional AMs. In addition, the proposed method without the prompted text has a fluency score performance comparable to that of the method with prompted text.
ISSN:1225-6463
2233-7326