# GM-TCNet: Gated Multi-scale Temporal Convolutional Network using Emotion Causality for Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2210.15834 · HTML: https://mlsanity.com/paper/2210.15834/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 81 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [SEGAN: Speech Enhancement Generative Adversarial Network](https://mlsanity.com/paper/1703.09452/) (2017)
- [Recent Advances in Recurrent Neural Networks](https://mlsanity.com/paper/1801.01078/) (2017)
- [MsEmoTTS: Multi-scale emotion transfer, prediction, and control for emotional speech synthesis](https://mlsanity.com/paper/2201.06460/) (2022)
- [On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges](https://mlsanity.com/paper/2201.06716/) (2022)
- [Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality](https://mlsanity.com/paper/2103.06541/) (2021)
- [FurcaNeXt: End-to-end monaural speech separation with dynamic gated dilated temporal convolutional networks](https://mlsanity.com/paper/1902.04891/) (2019)
- [GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2007.03519/) (2020)
- [Learning The Sequential Temporal Information with Recurrent Neural Networks](https://mlsanity.com/paper/1807.02857/) (2018)

## Built on by (most cited first, 6 of 6)

- [Temporal Modeling Matters: A Novel Temporal Emotional Modeling Approach for Speech Emotion Recognition](https://mlsanity.com/paper/2211.08233/) (2022)
- [Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2308.02190/) (2023)
- [Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition](https://mlsanity.com/paper/2405.00307/) (2024)
- [Multi-modal Speech Emotion Recognition via Feature Distribution Adaptation Network](https://mlsanity.com/paper/2410.22023/) (2024)
- [EmoHRNet: High-Resolution Neural Network Based Speech Emotion Recognition](https://mlsanity.com/paper/2510.06072/) (2025)
- [Deep Learning Approaches for 3D Medical Scene Completion: From Geometric Modeling to Generative Paradigms](https://mlsanity.com/paper/2606.24180/) (2026)

## Abstract (arXiv)

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.