Arabic Speech Emotion Recognition Employing Wav2vec2.0 and HuBERT Based on BAVED Dataset
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Abstract (arXiv)
Recently, there have been tremendous research outcomes in the fields of speech recognition and natural language processing. This is due to the well-developed multi-layers deep learning paradigms such as wav2vec2.0, Wav2vecU, WavBERT, and HuBERT that provide better representation learning and high information capturing. Such paradigms run on hundreds of unlabeled data, then fine-tuned on a small dataset for specific tasks. This paper introduces a deep learning constructed emotional recognition model for Arabic speech dialogues. The developed model employs the state of the art audio representations include wav2vec2.0 and HuBERT. The experiment and performance results of our model overcome the previous known outcomes.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 4 of its 23 references that are in the map. First posted on arXiv 9 Oct 2021.
Builds on
4 of 23 references.
References · 4
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,301
- 2021Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Languagecited by 12
- 2020Efficient Arabic emotion recognition using deep neural networkscited by 3
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Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 2; 2025: 0; 2026: 2.
9 papers.
- 2023Wav2vec-based Detection and Severity Level Classification of Dysarthria from Speechcited by 14
- 2022Phoneme Segmentation Using Self-Supervised Speech Modelscited by 11
- 2024AVI-Talking: Learning Audio-Visual Instructions for Expressive 3D Talking Face Generationcited by 8
- 2022Speech Emotion: Investigating Model Representations, Multi-Task Learning and Knowledge Distillationcited by 5
- 2023Investigation of Self-supervised Pre-trained Models for Classification of Voice Quality from Speech and Neck Surface Accelerometer Signalscited by 2
- 2021Towards an Efficient Voice Identification Using Wav2Vec2.0 and HuBERT Based on the Quran Reciters Datasetcited by 0
- 2024Active Learning with Task Adaptation Pre-training for Speech Emotion Recognitioncited by 0
- 2026voice2mode: Phonation Mode Classification in Singing using Self-Supervised Speech Modelscited by 0
- 2026Towards Robust Arabic Speech Emotion Recognition with Deep Learningcited by 0
- 2026Towards Robust Arabic Speech Emotion Recognition with Deep Learning9 Jun 2026
- 2026voice2mode: Phonation Mode Classification in Singing using Self-Supervised Speech Models14 Feb 2026
- 2024Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition1 May 2024
- 2024AVI-Talking: Learning Audio-Visual Instructions for Expressive 3D Talking Face Generation25 Feb 2024
- 2023Wav2vec-based Detection and Severity Level Classification of Dysarthria from Speech25 Sep 2023
- 2023Investigation of Self-supervised Pre-trained Models for Classification of Voice Quality from Speech and Neck Surface Accelerometer Signals6 Aug 2023
- 2022Phoneme Segmentation Using Self-Supervised Speech Models2 Nov 2022
- 2022Speech Emotion: Investigating Model Representations, Multi-Task Learning and Knowledge Distillation2 Jul 2022
- 2021Towards an Efficient Voice Identification Using Wav2Vec2.0 and HuBERT Based on the Quran Reciters Dataset11 Nov 2021