# Searching for Effective Preprocessing Method and CNN-based Architecture with Efficient Channel Attention on Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2409.04007 · HTML: https://mlsanity.com/paper/2409.04007/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 65 references that are in the map.
- First posted on arXiv 6 Sep 2024.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [BAM: Bottleneck Attention Module](https://mlsanity.com/paper/1807.06514/) (2018)
- [How Much Position Information Do Convolutional Neural Networks Encode?](https://mlsanity.com/paper/2001.08248/) (2020)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context](https://mlsanity.com/paper/2005.03191/) (2020)
- [Model Complexity of Deep Learning: A Survey](https://mlsanity.com/paper/2103.05127/) (2021)
- [PPGAN: Privacy-preserving Generative Adversarial Network](https://mlsanity.com/paper/1910.02007/) (2019)
- [CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters](https://mlsanity.com/paper/2203.15331/) (2022)
- [Temporal Modeling Matters: A Novel Temporal Emotional Modeling Approach for Speech Emotion Recognition](https://mlsanity.com/paper/2211.08233/) (2022)
- [Vesper: A Compact and Effective Pretrained Model for Speech Emotion Recognition](https://mlsanity.com/paper/2307.10757/) (2023)
- [Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition](https://mlsanity.com/paper/2110.03435/) (2021)
- [Speech Emotion Recognition with Multiscale Area Attention and Data Augmentation](https://mlsanity.com/paper/2102.01813/) (2021)
- [Effects of Number of Filters of Convolutional Layers on Speech Recognition Model Accuracy](https://mlsanity.com/paper/2102.02326/) (2021)

## Abstract (arXiv)

Speech emotion recognition (SER) classifies human emotions in speech with a computer model. Recently, performance in SER has steadily increased as deep learning techniques have adapted. However, unlike many domains that use speech data, data for training in the SER model is insufficient. This causes overfitting of training of the neural network, resulting in performance degradation. In fact, successful emotion recognition requires an effective preprocessing method and a model structure that efficiently uses the number of weight parameters. In this study, we propose using eight dataset versions with different frequency-time resolutions to search for an effective emotional speech preprocessing method. We propose a 6-layer convolutional neural network (CNN) model with efficient channel attention (ECA) to pursue an efficient model structure. In particular, the well-positioned ECA blocks can improve channel feature representation with only a few parameters. With the interactive emotional dyadic motion capture (IEMOCAP) dataset, increasing the frequency resolution in preprocessing emotional speech can improve emotion recognition performance. Also, ECA after the deep convolution layer can effectively increase channel feature representation. Consequently, the best result (79.37UA 79.68WA) can be obtained, exceeding the performance of previous SER models. Furthermore, to compensate for the lack of emotional speech data, we experiment with multiple preprocessing data methods that augment trainable data preprocessed with all different settings from one sample. In the experiment, we can achieve the highest result (80.28UA 80.46WA).