# Arabic Speech Emotion Recognition Employing Wav2vec2.0 and HuBERT Based on BAVED Dataset

arXiv: https://arxiv.org/abs/2110.04425 · HTML: https://mlsanity.com/paper/2110.04425/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 9 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language](https://mlsanity.com/paper/2109.10255/) (2021)
- [Efficient Arabic emotion recognition using deep neural networks](https://mlsanity.com/paper/2011.00346/) (2020)

## Built on by (most cited first, 10 of 10)

- [Wav2vec-based Detection and Severity Level Classification of Dysarthria from Speech](https://mlsanity.com/paper/2309.14107/) (2023)
- [Phoneme Segmentation Using Self-Supervised Speech Models](https://mlsanity.com/paper/2211.01461/) (2022)
- [AVI-Talking: Learning Audio-Visual Instructions for Expressive 3D Talking Face Generation](https://mlsanity.com/paper/2402.16124/) (2024)
- [Fast Yet Effective Speech Emotion Recognition with Self-distillation](https://mlsanity.com/paper/2210.14636/) (2022)
- [Speech Emotion: Investigating Model Representations, Multi-Task Learning and Knowledge Distillation](https://mlsanity.com/paper/2207.03334/) (2022)
- [Investigation of Self-supervised Pre-trained Models for Classification of Voice Quality from Speech and Neck Surface Accelerometer Signals](https://mlsanity.com/paper/2308.03226/) (2023)
- [Towards an Efficient Voice Identification Using Wav2Vec2.0 and HuBERT Based on the Quran Reciters Dataset](https://mlsanity.com/paper/2111.06331/) (2021)
- [Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition](https://mlsanity.com/paper/2405.00307/) (2024)
- [voice2mode: Phonation Mode Classification in Singing using Self-Supervised Speech Models](https://mlsanity.com/paper/2602.13928/) (2026)
- [Towards Robust Arabic Speech Emotion Recognition with Deep Learning](https://mlsanity.com/paper/2606.10278/) (2026)

## 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.