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Recently, there have been tremendous research outcomes in the fields of speech recognition and natural language processing.
M. Meddeb, H. Karray, and A. Alimi, “Speech emotion recognition based on arabic features,” 2015 15th IEEE International Conference In Intelligent Systems Design and Applications (ISDA)
2015
Earlier work this paper cites.
A. H. Meftah, Y. A. Alotaibi, and S.-A. Selouani, “Ksuemotions ldc2017s12.” Web Download. Philadelphia: Linguistic Data Consortium, 2017 https://catalog.ldc.upenn.edu/LDC2017S12, 2017
2017
Earlier work this paper cites.
A. Satt, S. Rozenberg, and R. Hoory, “Efficient emotion recognition from speech using deep learning on spectrograms,” in Proc. Interspeech 2017
2017
Earlier work this paper cites.
P. Yenigalla, A. Kumar, S. Tripathi, C. Singh, S. Kar, and J. Vepa, “Speech emotion recognition using spectrogram & phoneme embedding,” In Proceedings of the INTERSPEECH, Hyderabad, India
2018
Earlier work this paper cites.
S. Zhang, S. Zhang, T. Huang, and W. Gao, “Speech emotion recognition using deep convolutional neural network and discriminant temporal pyramid matching,” IEEE Trans. Multimed
2018
Earlier work this paper cites.
S. Klaylat, Z. Osman, L. Hamandi, and R. Zantout, “Emotion recognition in arabic speech,” Analog Integr Circ Sig Process
2018
Earlier work this paper cites.
A. Almahdawi and W. Teahan, “A new arabic dataset for emotion recognition. in: Arai k., bhatia r., kapoor s. (eds),” Intelligent Computing. CompCom 2019, Advances in Intelligent Systems and Computing, vol 998. Springer, Cham
2019
Earlier work this paper cites.
A. Aouf, “Basic arabic vocal emotions dataset (baved) - github,” https://github.com/40uf411/Basic-Arabic-Vocal-Emotions-Dataset
2019
Earlier work this paper cites.
R. Khalil, E. Jones, M. Babar, T. Jan, M. Zafar, and T. Alhussain, “Speech emotion recognition using deep learning techniques: A review,” 2019
2019
Earlier work this paper cites.
Y. Hifny and A. Ali, “Efficient arabic emotion recognition using deep neural networks,” in IEEE Intern. Conf. on Acoustics, Speech and Signal Processing (ICASSP): https://github.com/qcri/deepemotion
2019
Cited alongside, same era.
Y. Hifny and A. Ali, “Efficient arabic emotion recognition using deep neural networks,” IEEE International Conf. on Acoustics, Speech and Signal Processing (ICASSP)
2019
Cited alongside, same era.
S. Klaylat, “Arabic natural audio dataset,” 2019
2019
Cited alongside, same era.
F. A. Shaqra, R. Duwairi, and M. Al-Ayyoub, “The audio-visual arabic dataset for natural emotions,” 7th International Conference on Future Internet of Things and Cloud (FiCloud)
2019
Cited alongside, same era.
L. Abdel-Hamid, “Egyptian arabic speech emotion recognition using prosodic, spectral and wavelet features,” Speech Commun
2020
I. Shahin, A. B. Nassif, N. Nemmour, A. Elnagar, A. Alhudhaif, and K. Polat, “Novel hybrid dnn approaches for speaker verification in emotional and stressful talking environments,” Neural Computing and Applications
2021
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F. M. P. del Arco1, S. Halat, S. Padó, and R. Klinger, “Multi-task learning with sentiment, emotion, and target detection to recognize hate speech and offensive language,” Forum for Information Retrieval Evaluation, Virtual Event
2021
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E. Lieskovská, M. Jakubec, R. Jarina, and M. Chmulík, “A review on speech emotion recognition using deep learning and attention mechanism,” electronics
2021
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W. Z. Zheng and Y. Zong, “Multi-scale discrepancy adversarial network for crosscorpus speech emotion recognition,” Virtual Real. Intell. Hardw
2021
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Cited alongside, same era.
A. Baevski, H. Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representations,” CoRR · 2020
Cited alongside, same era.
Elgeish, “https://huggingface.co/elgeish/wav2vec2-large-xlsr-53-arabic,” 2020
2020
Cited alongside, same era.
K. Noh, C. Jeong, J. Lim, S. Chung, G. Kim, J. Lim, and H. Jeong, “Multi-path and group-loss-based network for speech emotion recognition in multi-domain datasets,” Sensors
2021
Cited alongside, same era.
R. Y. Cherif, A. Moussaoui, N. Frahta, and M. Berrimi, “Effective speech emotion recognition using deep learning approaches for algerian dialect,” Intern. Conf. of Women in Data Science at Taif University (WiDSTaif)
2021
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W.-N. Hsu, B. Bolte, Y.-H. H. Tsai, K. Lakhotia, R. Salakhutdinov, and A. Mohamed, “Hubert: Self-supervised speech representation learning by masked prediction of hidden units,” 2021
2021
Closest in time.
CommonVoice, “https://commonvoice.mozilla.org/en/datasets,” ar-137h-2021-07-21
2021
Closest in time.
N. Halabi, “Arabic speech corpus,” http://ar.arabicspeechcorpus.com/
2021
Closest in time.