Cited by 125Emotion and Mood RecognitionDomain Adaptation and Few-Shot LearningSpeech Recognition and Synthesis

Universum Autoencoder-Based Domain Adaptation for Speech Emotion Recognition

Deng, Jun, Xu, Xinzhou, Zhang, Zixing, Fruhholz, Sascha, Schuller, Bjorn·2017·IEEE Signal Processing Letters

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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Crossref · 10.1109/lsp.2017.2672753

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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
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › 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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