# Attention Based Fully Convolutional Network for Speech Emotion Recognition

arXiv: https://arxiv.org/abs/1806.01506 · HTML: https://mlsanity.com/paper/1806.01506/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2018.

## Built on by (most cited first, 23 of 23)

- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognition](https://mlsanity.com/paper/2110.06309/) (2021)
- [Machine Learning for Stuttering Identification: Review, Challenges and Future Directions](https://mlsanity.com/paper/2107.04057/) (2021)
- [Emotion Based Hate Speech Detection using Multimodal Learning](https://mlsanity.com/paper/2202.06218/) (2022)
- [LSSED: a large-scale dataset and benchmark for speech emotion recognition](https://mlsanity.com/paper/2102.01754/) (2021)
- [EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2103.08310/) (2021)
- [Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings](https://mlsanity.com/paper/2112.13416/) (2021)
- [Semi-FedSER: Semi-supervised Learning for Speech Emotion Recognition On Federated Learning using Multiview Pseudo-Labeling](https://mlsanity.com/paper/2203.08810/) (2022)
- [VEATIC: Video-based Emotion and Affect Tracking in Context Dataset](https://mlsanity.com/paper/2309.06745/) (2023)
- [Emotion Recognition In Persian Speech Using Deep Neural Networks](https://mlsanity.com/paper/2204.13601/) (2022)
- [Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated Learning](https://mlsanity.com/paper/2202.02611/) (2022)
- [Facial Affect Recognition based on Transformer Encoder and Audiovisual Fusion for the ABAW5 Challenge](https://mlsanity.com/paper/2303.09158/) (2023)
- [Multimodal Fusion Method with Spatiotemporal Sequences and Relationship Learning for Valence-Arousal Estimation](https://mlsanity.com/paper/2403.12425/) (2024)
- [Imperceptible Rhythm Backdoor Attacks: Exploring Rhythm Transformation for Embedding Undetectable Vulnerabilities on Speech Recognition](https://mlsanity.com/paper/2406.10932/) (2024)
- [Speech Emotion Recognition with Global-Aware Fusion on Multi-scale Feature Representation](https://mlsanity.com/paper/2204.05571/) (2022)
- [Speech Emotion Recognition via an Attentive Time-Frequency Neural Network](https://mlsanity.com/paper/2210.12430/) (2022)
- [GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations](https://mlsanity.com/paper/2503.20919/) (2025)
- [Facial Affect Recognition based on Multi Architecture Encoder and Feature Fusion for the ABAW7 Challenge](https://mlsanity.com/paper/2407.12258/) (2024)
- [Improving Speaker-independent Speech Emotion Recognition Using Dynamic Joint Distribution Adaptation](https://mlsanity.com/paper/2401.09752/) (2024)
- [Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition](https://mlsanity.com/paper/2309.12714/) (2023)
- [Speech and Text-Based Emotion Recognizer](https://mlsanity.com/paper/2312.11503/) (2023)
- [Partial Federated Learning](https://mlsanity.com/paper/2403.01615/) (2024)
- [emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion Recognition](https://mlsanity.com/paper/2403.14083/) (2024)

## Abstract (arXiv)

Speech emotion recognition is a challenging task for three main reasons: 1) human emotion is abstract, which means it is hard to distinguish; 2) in general, human emotion can only be detected in some specific moments during a long utterance; 3) speech data with emotional labeling is usually limited. In this paper, we present a novel attention based fully convolutional network for speech emotion recognition. We employ fully convolutional network as it is able to handle variable-length speech, free of the demand of segmentation to keep critical information not lost. The proposed attention mechanism can make our model be aware of which time-frequency region of speech spectrogram is more emotion-relevant. Considering limited data, the transfer learning is also adapted to improve the accuracy. Especially, it's interesting to observe obvious improvement obtained with natural scene image based pre-trained model. Validated on the publicly available IEMOCAP corpus, the proposed model outperformed the state-of-the-art methods with a weighted accuracy of 70.4% and an unweighted accuracy of 63.9% respectively.