تقرير
GM-TCNet: Gated Multi-scale Temporal Convolutional Network using Emotion Causality for Speech Emotion Recognition
| العنوان: | GM-TCNet: Gated Multi-scale Temporal Convolutional Network using Emotion Causality for Speech Emotion Recognition |
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| المؤلفون: | Ye, Jia-Xin, Wen, Xin-Cheng, Wang, Xuan-Ze, Xu, Yong, Luo, Yan, Wu, Chang-Li, Chen, Li-Yan, Liu, Kun-Hong |
| المصدر: | speech communication, 145, November 2022, 21-35 |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Sound, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, Electrical Engineering and Systems Science - Audio and Speech Processing |
| الوصف: | In human-computer interaction, Speech Emotion Recognition (SER) plays an essential role in understanding the user's intent and improving the interactive experience. While similar sentimental speeches own diverse speaker characteristics but share common antecedents and consequences, an essential challenge for SER is how to produce robust and discriminative representations through causality between speech emotions. In this paper, we propose a Gated Multi-scale Temporal Convolutional Network (GM-TCNet) to construct a novel emotional causality representation learning component with a multi-scale receptive field. GM-TCNet deploys a novel emotional causality representation learning component to capture the dynamics of emotion across the time domain, constructed with dilated causal convolution layer and gating mechanism. Besides, it utilizes skip connection fusing high-level features from different gated convolution blocks to capture abundant and subtle emotion changes in human speech. GM-TCNet first uses a single type of feature, mel-frequency cepstral coefficients, as inputs and then passes them through the gated temporal convolutional module to generate the high-level features. Finally, the features are fed to the emotion classifier to accomplish the SER task. The experimental results show that our model maintains the highest performance in most cases compared to state-of-the-art techniques. Comment: The source code is available at: https://github.com/Jiaxin-Ye/GM-TCNet |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1016/j.specom.2022.07.005 |
| URL الوصول: | http://arxiv.org/abs/2210.15834 |
| رقم الأكسشن: | edsarx.2210.15834 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1016/j.specom.2022.07.005 |
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