Facing realism in spontaneous emotion recognition from speech: feature enhancement by autoencoder with LSTM neural networks
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
In: Understanding speech processing in humans and machines: 17th Annual Conference of the International Speech Communication Association (INTERSPEECH 2016), San Francisco, California, USA, 8-12 September 2016; Volume 5, S. 3593-3597
- Event
-
Veröffentlichung
- (where)
-
Augsburg
- (who)
-
Universität Augsburg
- (when)
-
2016
- Event
-
Veröffentlichung
- (who)
-
ISCA
- (when)
-
2016
- Creator
- DOI
-
10.21437/Interspeech.2016-998
- URN
-
urn:nbn:de:bvb:384-opus4-721470
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
2025-08-15T07:28:20+0200
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Zhang, Zixing
- Ringeval, Fabien
- Han, Jing
- Deng, Jun
- Marchi, Erik
- Schuller, Björn
- Universität Augsburg
- ISCA
Time of origin
- 2016
Other Objects (12)
Autoencoder-based unsupervised domain adaptation for speech emotion recognition
Universum autoencoder-based domain adaptation for speech emotion recognition
Cooperative learning and its application to emotion recognition from speech
Recognizing emotions from whispered speech based on acoustic feature transfer learning
Distributing recognition in computational paralinguistics
Sparse autoencoder-based feature transfer learning for speech emotion recognition
Exploitation of phase-based features for whispered speech emotion recognition
Semisupervised autoencoders for speech emotion recognition
Introducing shared-hidden-layer autoencoders for transfer learning and their application in acoustic emotion recognition
Cluster Preface: Biomimetic Synthesis
Exploring zero-shot emotion recognition in speech using semantic-embedding prototypes
Leveraging Unlabeled Data for Emotion Recognition With Enhanced Collaborative Semi-Supervised Learning
Autoencoder-based unsupervised domain adaptation for speech emotion recognition
Universum autoencoder-based domain adaptation for speech emotion recognition
Cooperative learning and its application to emotion recognition from speech
Recognizing emotions from whispered speech based on acoustic feature transfer learning
Distributing recognition in computational paralinguistics
Sparse autoencoder-based feature transfer learning for speech emotion recognition
Exploitation of phase-based features for whispered speech emotion recognition
Semisupervised autoencoders for speech emotion recognition
Introducing shared-hidden-layer autoencoders for transfer learning and their application in acoustic emotion recognition
Cluster Preface: Biomimetic Synthesis
Exploring zero-shot emotion recognition in speech using semantic-embedding prototypes
Leveraging Unlabeled Data for Emotion Recognition With Enhanced Collaborative Semi-Supervised Learning
Autoencoder-based unsupervised domain adaptation for speech emotion recognition
Universum autoencoder-based domain adaptation for speech emotion recognition
Cooperative learning and its application to emotion recognition from speech
Recognizing emotions from whispered speech based on acoustic feature transfer learning
Distributing recognition in computational paralinguistics
Sparse autoencoder-based feature transfer learning for speech emotion recognition
Exploitation of phase-based features for whispered speech emotion recognition
Semisupervised autoencoders for speech emotion recognition
Introducing shared-hidden-layer autoencoders for transfer learning and their application in acoustic emotion recognition
Cluster Preface: Biomimetic Synthesis
Exploring zero-shot emotion recognition in speech using semantic-embedding prototypes