Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/8349
Title: English speaking proficiency assessment using speech and electroencephalography signals
Authors: Hanani, Abualsoud 
Abusara, Yanal 
Maher, Bisan 
Musleh, Inas 
Keywords: Multivariate analysis;Audio features;EEG;EEG features;English language - Ability testing;Support vector machines
Issue Date: 2022
Publisher: International Journal of Electrical and Computer Engineering
Abstract: In this paper, the English speaking proficiency level of non-native English speakers was 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.
URI: http://hdl.handle.net/20.500.11889/8349
DOI: 10.11591/ijece.v12i3.pp2501-2508
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