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All Journal International Journal of Electrical and Computer Engineering
Bisan Maher
Birzeit University
Computer Science & IT Electrical & Electronics Engineering

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English speaking proficiency assessment using speech and electroencephalography signals Abualsoud Hanani; Yanal Abusara; Bisan Maher; Inas Musleh
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2501-2508

Abstract

In this paper, the English speaking proficiency level of non-native English speakerswas automatically estimated as high, medium, or low performance. For this purpose, the speech of 142 non-native English speakers was recorded and electroencephalography (EEG) signals of 58 of them were recorded while speaking in English. Two systems were proposed for estimating the English proficiency level of the speaker; one used 72 audio features, extracted from speech signals, and the other used 112 features extracted from EEG signals. Multi-class support vector machines (SVM) was used for training and testing both systems using a cross-validation strategy. The speech-based system outperformed the EEG system with 68% accuracy on 60 testing audio recordings, compared with 56% accuracy on 30 testing EEG recordings.
Co-Authors Abualsoud Hanani Inas Musleh Yanal Abusara