Cross-Corpus Training Strategy for Speech Emotion Recognition Using Self-Supervised Representations Article Swipe
Miguel A. Pastor
,
Dayana Ribas
,
Alfonso Ortega
,
Antonio Miguel
,
Eduardo Lleida
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13169062
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13169062
Speech Emotion Recognition (SER) plays a crucial role in applications involving human-machine interaction. However, the scarcity of suitable emotional speech datasets presents a major challenge for accurate SER systems. Deep Neural Network (DNN)-based solutions currently in use require substantial labelled data for successful training. Previous studies have proposed strategies to expand the training set in this framework by leveraging available emotion speech corpora. This paper assesses the impact of a cross-corpus training extension for a SER system using self-supervised (SS) representations, namely HuBERT and WavLM. The feasibility of training systems with just a few minutes of in-domain audio is also analyzed. The experimental results demonstrate that augmenting the training set with EmoDB (German), RAVDESS, and CREMA-D (English) datasets leads to improved SER accuracy on the IEMOCAP dataset. By combining a cross-corpus training extension and SS representations, state-of-the-art performance is achieved. These findings suggest that the cross-corpus strategy effectively addresses the scarcity of labelled data and enhances the performance of SER systems.
Related Topics
Concepts
Computer science
Scarcity
Training set
Emotion recognition
Artificial intelligence
Speech recognition
Set (abstract data type)
Labeled data
Natural language processing
Domain (mathematical analysis)
Deep neural networks
Training (meteorology)
Extension (predicate logic)
Machine learning
Artificial neural network
Mathematical analysis
Economics
Microeconomics
Meteorology
Physics
Programming language
Mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app13169062
- https://www.mdpi.com/2076-3417/13/16/9062/pdf?version=1691495938
- OA Status
- gold
- Cited By
- 17
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385655423
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4385655423Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app13169062Digital Object Identifier
- Title
-
Cross-Corpus Training Strategy for Speech Emotion Recognition Using Self-Supervised RepresentationsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-08Full publication date if available
- Authors
-
Miguel A. Pastor, Dayana Ribas, Alfonso Ortega, Antonio Miguel, Eduardo LleidaList of authors in order
- Landing page
-
https://doi.org/10.3390/app13169062Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/13/16/9062/pdf?version=1691495938Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2076-3417/13/16/9062/pdf?version=1691495938Direct OA link when available
- Concepts
-
Computer science, Scarcity, Training set, Emotion recognition, Artificial intelligence, Speech recognition, Set (abstract data type), Labeled data, Natural language processing, Domain (mathematical analysis), Deep neural networks, Training (meteorology), Extension (predicate logic), Machine learning, Artificial neural network, Mathematical analysis, Economics, Microeconomics, Meteorology, Physics, Programming language, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
17Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 7, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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