Deep temporal clustering features for speech emotion recognition

Author:

Lin Wei-Cheng,Busso CarlosORCID

Funder

National Science Foundation

Publisher

Elsevier BV

Reference65 articles.

1. Study of dense network approaches for speech emotion recognition;Abdelwahab,2018

2. Abdelwahab, M., Busso, C., 2019. Active Learning for Speech Emotion Recognition using Deep Neural Network. In: International Conference on Affective Computing and Intelligent Interaction. ACII 2019, Cambridge, UK, pp. 441–447. http://dx.doi.org/10.1109/ACII.2019.8925524.

3. Asano, Y., Patrick, M., Rupprecht, C., Vedaldi, A., 2020. Labelling unlabelled videos from scratch with multi-modal self-supervision. In: Advances in Neural Information Processing Systems (NeurIPS 2020), vol. 33, Virtual, pp. 4660–4671.

4. Baevski, A., Zhou, Y., Mohamed, A., Auli, M., 2020. wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations. In: Advances in Neural Information Processing Systems (NeurIPS 2020), vol. 33, Virtual, pp. 12449–12460.

5. Class-level spectral features for emotion recognition;Bitouk;Speech Commun.,2010

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