# Learning Spontaneity to Improve Emotion Recognition In Speech

arXiv: https://arxiv.org/abs/1712.04753 · HTML: https://mlsanity.com/paper/1712.04753/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2017.

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

- [Explainable Human-centered Traits from Head Motion and Facial Expression Dynamics](https://mlsanity.com/paper/2302.09817/) (2023)
- [Implicit Design Choices and Their Impact on Emotion Recognition Model Development and Evaluation](https://mlsanity.com/paper/2309.03238/) (2023)

## Abstract (arXiv)

We investigate the effect and usefulness of spontaneity (i.e. whether a given speech is spontaneous or not) in speech in the context of emotion recognition. We hypothesize that emotional content in speech is interrelated with its spontaneity, and use spontaneity classification as an auxiliary task to the problem of emotion recognition. We propose two supervised learning settings that utilize spontaneity to improve speech emotion recognition: a hierarchical model that performs spontaneity detection before performing emotion recognition, and a multitask learning model that jointly learns to recognize both spontaneity and emotion. Through various experiments on the well known IEMOCAP database, we show that by using spontaneity detection as an additional task, significant improvement can be achieved over emotion recognition systems that are unaware of spontaneity. We achieve state-of-the-art emotion recognition accuracy (4-class, 69.1%) on the IEMOCAP database outperforming several relevant and competitive baselines.