# Improving Speech Emotion Recognition Through Focus and Calibration Attention Mechanisms

arXiv: https://arxiv.org/abs/2208.10491 · HTML: https://mlsanity.com/paper/2208.10491/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 21 Aug 2022.

## Builds on (most cited first, 13 of 13)

- [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)
- [Speaker Recognition from Raw Waveform with SincNet](https://mlsanity.com/paper/1808.00158/) (2018)
- [Streaming automatic speech recognition with the transformer model](https://mlsanity.com/paper/2001.02674/) (2020)
- [Normalized and Geometry-Aware Self-Attention Network for Image Captioning](https://mlsanity.com/paper/2003.08897/) (2020)
- [Squeeze-and-Attention Networks for Semantic Segmentation](https://mlsanity.com/paper/1909.03402/) (2019)
- [Speech Emotion Recognition with Dual-Sequence LSTM Architecture](https://mlsanity.com/paper/1910.08874/) (2019)
- [How Does Selective Mechanism Improve Self-Attention Networks?](https://mlsanity.com/paper/2005.00979/) (2020)
- [Speech Emotion Recognition Using Multi-hop Attention Mechanism](https://mlsanity.com/paper/1904.10788/) (2019)
- [Guiding Attention for Self-Supervised Learning with Transformers](https://mlsanity.com/paper/2010.02399/) (2020)
- [Efficient Speech Emotion Recognition Using Multi-Scale CNN and Attention](https://mlsanity.com/paper/2106.04133/) (2021)
- [Attention Driven Fusion for Multi-Modal Emotion Recognition](https://mlsanity.com/paper/2009.10991/) (2020)
- [Speech Emotion Recognition Using Quaternion Convolutional Neural Networks](https://mlsanity.com/paper/2111.00404/) (2021)

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

- [Learning Emotional Representations from Imbalanced Speech Data for Speech Emotion Recognition and Emotional Text-to-Speech](https://mlsanity.com/paper/2306.05709/) (2023)
- [Enhancing Speech Emotion Recognition through Segmental Average Pooling of Self-Supervised Learning Features](https://mlsanity.com/paper/2410.12416/) (2024)
- [Geometric Second-Order Feature Correlation Learning for Self-Supervised Speech Emotion Recognition](https://mlsanity.com/paper/2606.06550/) (2026)

## Abstract (arXiv)

Attention has become one of the most commonly used mechanisms in deep learning approaches. The attention mechanism can help the system focus more on the feature space's critical regions. For example, high amplitude regions can play an important role for Speech Emotion Recognition (SER). In this paper, we identify misalignments between the attention and the signal amplitude in the existing multi-head self-attention. To improve the attention area, we propose to use a Focus-Attention (FA) mechanism and a novel Calibration-Attention (CA) mechanism in combination with the multi-head self-attention. Through the FA mechanism, the network can detect the largest amplitude part in the segment. By employing the CA mechanism, the network can modulate the information flow by assigning different weights to each attention head and improve the utilization of surrounding contexts. To evaluate the proposed method, experiments are performed with the IEMOCAP and RAVDESS datasets. Experimental results show that the proposed framework significantly outperforms the state-of-the-art approaches on both datasets.