# Hybrid Data Augmentation and Deep Attention-based Dilated Convolutional-Recurrent Neural Networks for Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2109.09026 · HTML: https://mlsanity.com/paper/2109.09026/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 78 references that are in the map.
- First posted on arXiv 18 Sep 2021.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Adversarial Audio Synthesis](https://mlsanity.com/paper/1802.04208/) (2018)
- [Dataset Augmentation in Feature Space](https://mlsanity.com/paper/1702.05538/) (2017)
- [Dilated Recurrent Neural Networks](https://mlsanity.com/paper/1710.02224/) (2017)
- [End-to-End Multimodal Emotion Recognition using Deep Neural Networks](https://mlsanity.com/paper/1704.08619/) (2017)
- [Speech Emotion Recognition Using Multi-hop Attention Mechanism](https://mlsanity.com/paper/1904.10788/) (2019)
- [Sound event detection via dilated convolutional recurrent neural networks](https://mlsanity.com/paper/1911.10888/) (2019)
- [On Enhancing Speech Emotion Recognition using Generative Adversarial Networks](https://mlsanity.com/paper/1806.06626/) (2018)

## Built on by (most cited first, 3 of 3)

- [Learning Speech Emotion Representations in the Quaternion Domain](https://mlsanity.com/paper/2204.02385/) (2022)
- [Emotion Selectable End-to-End Text-based Speech Editing](https://mlsanity.com/paper/2212.10191/) (2022)
- [Memo2496: Expert-Annotated Dataset and Dual-view Adaptive Framework for Music Emotion Recognition](https://mlsanity.com/paper/2512.13998/) (2025)

## Abstract (arXiv)

Speech emotion recognition (SER) has been one of the significant tasks in Human-Computer Interaction (HCI) applications. However, it is hard to choose the optimal features and deal with imbalance labeled data. In this article, we investigate hybrid data augmentation (HDA) methods to generate and balance data based on traditional and generative adversarial networks (GAN) methods. To evaluate the effectiveness of HDA methods, a deep learning framework namely (ADCRNN) is designed by integrating deep dilated convolutional-recurrent neural networks with an attention mechanism. Besides, we choose 3D log Mel-spectrogram (MelSpec) features as the inputs for the deep learning framework. Furthermore, we reconfigure a loss function by combining a softmax loss and a center loss to classify the emotions. For validating our proposed methods, we use the EmoDB dataset that consists of several emotions with imbalanced samples. Experimental results prove that the proposed methods achieve better accuracy than the state-of-the-art methods on the EmoDB with 87.12% and 88.47% for the traditional and GAN-based methods, respectively.