# Cross Lingual Cross Corpus Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2003.07996 · HTML: https://mlsanity.com/paper/2003.07996/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [Speech Emotion Recognition Using Quaternion Convolutional Neural Networks](https://mlsanity.com/paper/2111.00404/) (2021)
- [Emotion Recognition In Persian Speech Using Deep Neural Networks](https://mlsanity.com/paper/2204.13601/) (2022)
- [Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction](https://mlsanity.com/paper/2204.11382/) (2022)
- [Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Search](https://mlsanity.com/paper/2211.08237/) (2022)
- [Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages](https://mlsanity.com/paper/2307.10814/) (2023)

## Abstract (arXiv)

The majority of existing speech emotion recognition models are trained and evaluated on a single corpus and a single language setting. These systems do not perform as well when applied in a cross-corpus and cross-language scenario. This paper presents results for speech emotion recognition for 4 languages in both single corpus and cross corpus setting. Additionally, since multi-task learning (MTL) with gender, naturalness and arousal as auxiliary tasks has shown to enhance the generalisation capabilities of the emotion models, this paper introduces language ID as another auxiliary task in MTL framework to explore the role of spoken language on emotion recognition which has not been studied yet.