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Abdelmaksoud, E. (2021). Arabic Automatic Speech Recognition Based on Emotion Detection. The Egyptian Journal of Language Engineering, 8(1), 17-26. doi: 10.21608/ejle.2020.49690.1016
Engy Ragaei Abdelmaksoud. "Arabic Automatic Speech Recognition Based on Emotion Detection". The Egyptian Journal of Language Engineering, 8, 1, 2021, 17-26. doi: 10.21608/ejle.2020.49690.1016
Abdelmaksoud, E. (2021). 'Arabic Automatic Speech Recognition Based on Emotion Detection', The Egyptian Journal of Language Engineering, 8(1), pp. 17-26. doi: 10.21608/ejle.2020.49690.1016
Abdelmaksoud, E. Arabic Automatic Speech Recognition Based on Emotion Detection. The Egyptian Journal of Language Engineering, 2021; 8(1): 17-26. doi: 10.21608/ejle.2020.49690.1016

Arabic Automatic Speech Recognition Based on Emotion Detection

Article 2, Volume 8, Issue 1, April 2021, Page 17-26  XML PDF (1.25 MB)
Document Type: Original Article
DOI: 10.21608/ejle.2020.49690.1016
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Author
Engy Ragaei Abdelmaksoud email
Basic science, Faculty of Computers and Informayion, Fayoum University
Abstract
This work presents a novel emotion recognition via automatic speech recognition (ASR) using a deep feed-forward neural network (DFFNN) for Arabic speech. We present results for the recognition of the three emotions happy, angry, and surprised. The Arabic natural audio dataset (ANAD) is used. Twenty-five low-level descriptors (LLDs) are extracted from the audio signals. Different combination of extracted features is examined. Also, the effect of using the principal component analysis (PCA) technique for dimensionality reduction is examined. For the classification stage, DFFNN is used. Also, the problem of imbalances samples in the dataset is managed by using the borderline-synthetic minority over-sampling technique (B-SMOTE). It is shown from the results that the best accuracy is obtained when applying PCA on the extracted features is 98.56 %. Also, the accuracy is 98.33 % when using the combination of all the extracted features. This result is not too much different from the accuracy of using PCA. It is followed by the accuracy of using MFCC and LSF which is 97.79 %. It is noticed that the accuracy is 95.63 % when using LSF features which shows that they are dominant features. The obtained results showed an improvement compared to previous studies.
Keywords
Arabic speech emotion recognition; Arabic natural audio dataset; deep feed-forward neural network; borderline- synthetic minority over-sampling technique; PCA
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