Exploring deep spectrum representations via attention-based recurrent and convolutional neural networks for speech emotion recognition
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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In: IEEE Access, 7, S. 97515-97525
- Event
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Veröffentlichung
- (where)
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Augsburg
- (who)
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Universität Augsburg
- (when)
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2019
- Event
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Veröffentlichung
- (where)
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New York, NY
- (who)
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Institute of Electrical and Electronics Engineers (IEEE)
- (when)
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2019
- Creator
- DOI
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10.1109/access.2019.2928625
- URN
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urn:nbn:de:bvb:384-opus4-611451
- Rights
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Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:36 AM CEST
Data provider
This object is provided by:
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Zhao, Ziping
- Bao, Zhongtian
- Zhao, Yiqin
- Zhang, Zixing
- Cummins, Nicholas
- Ren, Zhao
- Schuller, Björn
- Universität Augsburg
- Institute of Electrical and Electronics Engineers (IEEE)
Time of origin
- 2019
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Autonomous emotion learning in speech: a view of zero-shot speech emotion recognition
Hierarchical attention-based temporal convolutional networks for EEG-based emotion recognition
A two-dimensional framework of multiple kernel subspace learning for recognizing emotion in speech
Self-attention transfer networks for speech emotion recognition
Automatic assessment of depression from speech via a hierarchical attention transfer network and attention autoencoders
The university of passau open emotion recognition system for the multimodal emotion challenge
Emotion Recognition in Speech with Latent Discriminative Representations Learning