The DOI matches its Crossref record; 28 of the 30 references it cites were found in a registry or an index.
Universum Autoencoder-Based Domain Adaptation for Speech Emotion Recognition
Abstract
One of the serious obstacles to the applications of speech emotion recognition systems in real-life settings is the lack of generalization of the emotion classifiers. Many recognition systems often present a dramatic drop in performance when tested on speech data obtained from different speakers, acoustic environments, linguistic content, and domain conditions. In this letter, we propose a novel unsupervised domain adaptation model, called Universum autoencoders, to improve the performance of the systems evaluated in mismatched training and test conditions. To address the mismatch, our proposed model not only learns discriminative information from labeled data, but also learns to incorporate the prior knowledge from unlabeled data into the learning. Experimental results on the labeled Geneva Whispered Emotion Corpus database plus other three unlabeled databases demonstrate the effectiveness of the proposed method when compared to other domain adaptation methods.
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Speech Emotion Recognition Using Deep Learning Techniques: A Review
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Metadata
- Authors
- Deng, J., Xu, X., Zhang, Z., Fruhholz, S., & Schuller, B.
- Affiliations
- University of Passau (DE)
- Technical University of Munich (DE)
- Venue
- IEEE Signal Processing Letters
- Published
- 2017-04
- Volume
- 24
- Issue
- 4
- Pages
- 500-504
- ISSN
- 1070-9908 (ISSN-L)
- 1558-2361
- OpenAlex
- W2589599921
- Cited by
- 125
- References
- 30
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 6 · 2023: 13 · 2022: 20 · 2021: 26 · 2020: 15 · 2019: 19 · 2018: 13 · 2017: 6
- FWCI
- 18.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Signal Processing Letters (publishedVersion)
- OPUS (Augsburg University) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Funders
- BMBF (IKT2020, 16SV7213)
- Swiss National Science Foundation (PP00P1_157409/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- speech emotion recognition, domain adaptation, unsupervised domain adaptation, autoencoder, unlabeled data
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