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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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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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