# Multimodal Speech Emotion Recognition and Ambiguity Resolution

arXiv: https://arxiv.org/abs/1904.06022 · HTML: https://mlsanity.com/paper/1904.06022/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality Conversion](https://mlsanity.com/paper/2307.11584/) (2023)
- [Are you sure? Analysing Uncertainty Quantification Approaches for Real-world Speech Emotion Recognition](https://mlsanity.com/paper/2407.01143/) (2024)
- [Towards A Multi-agent System for Online Hate Speech Detection](https://mlsanity.com/paper/2105.01129/) (2021)
- [A General, Evolution-Inspired Reward Function for Social Robotics](https://mlsanity.com/paper/2202.00617/) (2022)

## Abstract (arXiv)

Identifying emotion from speech is a non-trivial task pertaining to the ambiguous definition of emotion itself. In this work, we adopt a feature-engineering based approach to tackle the task of speech emotion recognition. Formalizing our problem as a multi-class classification problem, we compare the performance of two categories of models. For both, we extract eight hand-crafted features from the audio signal. In the first approach, the extracted features are used to train six traditional machine learning classifiers, whereas the second approach is based on deep learning wherein a baseline feed-forward neural network and an LSTM-based classifier are trained over the same features. In order to resolve ambiguity in communication, we also include features from the text domain. We report accuracy, f-score, precision, and recall for the different experiment settings we evaluated our models in. Overall, we show that lighter machine learning based models trained over a few hand-crafted features are able to achieve performance comparable to the current deep learning based state-of-the-art method for emotion recognition.