Semi-Supervised Learning for Multimodal Speech and Emotion Recognition-Reference-Cited by-同舟云学术

Semi-Supervised Learning for Multimodal Speech and Emotion Recognition

Published:2021-10-18 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2021 International Conference on Multimodal Interaction
language:
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Author:

Li Yuanchao¹

Affiliation:

1. School of Informatics, University of Edinburgh, United Kingdom

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3462244.3481274

Reference48 articles.

1. Deep Lip Reading: A Comparison of Models and an Online Application

2. Emotion recognition based on EEG using LSTM recurrent neural network;Alhagry Salma;Emotion,2017

3. Speech Emotion Recognition from Spectrograms with Deep Convolutional Neural Network

4. Alexei Baevski Steffen Schneider and Michael Auli. 2019. vq-wav2vec: Self-supervised learning of discrete speech representations. arXiv preprint arXiv:1910.05453(2019). Alexei Baevski Steffen Schneider and Michael Auli. 2019. vq-wav2vec: Self-supervised learning of discrete speech representations. arXiv preprint arXiv:1910.05453(2019).

5. Alexei Baevski Henry Zhou Abdelrahman Mohamed and Michael Auli. 2020. wav2vec 2.0: A framework for self-supervised learning of speech representations. arXiv preprint arXiv:2006.11477(2020). Alexei Baevski Henry Zhou Abdelrahman Mohamed and Michael Auli. 2020. wav2vec 2.0: A framework for self-supervised learning of speech representations. arXiv preprint arXiv:2006.11477(2020).

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1. Toward Label-Efficient Emotion and Sentiment Analysis;Proceedings of the IEEE;2023-10

2. Enhancing Speech Emotion Recognition for Real-World Applications via ASR Integration;2023 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW);2023-09-10