Cross Lingual Cross Corpus Speech Emotion Recognition
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Mar 2020.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 1.
5 papers.
- 2021Speech Emotion Recognition Using Quaternion Convolutional Neural Networkscited by 11
- 2022Emotion Recognition In Persian Speech Using Deep Neural Networkscited by 7
- 2022Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extractioncited by 2
- 2022Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Searchcited by 1
- 2023Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languagescited by 1
- 2023Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages20 Jul 2023
- 2022Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Search31 Oct 2022
- 2022Emotion Recognition In Persian Speech Using Deep Neural Networks28 Apr 2022
- 2022Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction25 Apr 2022
- 2021Speech Emotion Recognition Using Quaternion Convolutional Neural Networks31 Oct 2021