# Two-stage dimensional emotion recognition by fusing predictions of acoustic and text networks using SVM

arXiv: https://arxiv.org/abs/2210.14495 · HTML: https://mlsanity.com/paper/2210.14495/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2022.

## Built on by (most cited first, 2 of 2)

- [Estimating the Uncertainty in Emotion Attributes using Deep Evidential Regression](https://mlsanity.com/paper/2306.06760/) (2023)
- [Beyond Text: LLM-Based Dimensional Emotion Evaluation in Multimodal Dialogue](https://mlsanity.com/paper/2609.39072/) (2026)

## Abstract (arXiv)

Automatic speech emotion recognition (SER) by a computer is a critical component for more natural human-machine interaction. As in human-human interaction, the capability to perceive emotion correctly is essential to take further steps in a particular situation. One issue in SER is whether it is necessary to combine acoustic features with other data such as facial expressions, text, and motion capture. This research proposes to combine acoustic and text information by applying a late-fusion approach consisting of two steps. First, acoustic and text features are trained separately in deep learning systems. Second, the prediction results from the deep learning systems are fed into a support vector machine (SVM) to predict the final regression score. Furthermore, the task in this research is dimensional emotion modeling because it can enable a deeper analysis of affective states. Experimental results show that this two-stage, late-fusion approach, obtains higher performance than that of any one-stage processing, with a linear correlation from one-stage to two-stage processing. This late-fusion approach improves previous early fusion results measured in concordance correlation coefficients score.