Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition
Open accessAbstract
In speech emotion recognition, training and test data used for system development usually tend to fit each other perfectly, but further 'similar' data may be available. Transfer learning helps to exploit such similar data for training despite the inherent dissimilarities in order to boost a recogniser's performance. In this context, this paper presents a sparse auto encoder method for feature transfer learning for speech emotion recognition. In our proposed method, a common emotion-specific mapping rule is learnt from a small set of labelled data in a target domain. Then, newly reconstructed data are obtained by applying this rule on the emotion-specific data in a different domain. The experimental results evaluated on six standard databases show that our approach significantly improves the performance relative to learning each source domain independently.
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Multiple studies have shown that transfer learning can improve model accuracy by leveraging additional sources of related knowledge.
“Multiple studies have shown that transfer learning can improve model accuracy by leveraging additional sources of related knowledge (e.g., from other paralinguistic tasks [136], various standard databases [137], and different affect representations [135]).”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [137]
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A Comprehensive Survey on Transfer Learning
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2020.3004555 · Cited by 6,678 · see citing works
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- Authors
- Deng, J., Zhang, Z., Marchi, E., & Schuller, B.
- Affiliations
- Technical University of Munich (DE)
- Venue
- 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction
- Publisher
- IEEE
- Published
- 2013-09
- Pages
- 511-516
- ISSN
- 2156-8103 (ISSN-L)
- OpenAlex
- W2050752817
- Cited by
- 366
- References
- 5
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 18 · 2023: 27 · 2022: 38 · 2021: 50 · 2020: 41 · 2019: 62 · 2018: 44 · 2017: 36 · 2016: 21 · 2015: 9 · 2014: 10
- FWCI
- 18.14
- Type
- conference paper
- Language
- en
- Open access
- green
- mediatum.ub.tum.de
- Licence
- other-oa
- Locations
- International Conference on Affective Computing and Intelligent Interaction and workshops (publishedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
- OPUS (Augsburg University) (submittedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- speech emotion recognition, sparse autoencoders, cross-domain transfer, domain adaptation
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