Combining frame and turn-level information for robust recognition of emotions within speech
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
In: INTERSPEECH 2007 - 8th Annual Conference of the International Speech Communication Association, Antwerp, Belgium, August 27-31, 2007, S. 2249-2252
- Classification
-
Informatik
- Event
-
Veröffentlichung
- (where)
-
Augsburg
- (who)
-
Universität Augsburg, ISCA Archive
- (when)
-
2007
- Creator
- URN
-
urn:nbn:de:bvb:384-opus4-767172
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.2025, 1:51 PM CET
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
- Vlasenko, Bogdan
- Schuller, Björn
- Wendemuth, Andreas
- Rigoll, Gerhard
- Universität Augsburg, ISCA Archive
Time of origin
- 2007
Other Objects (12)
Recognising interest in conversational speech - comparing bag of frames and supra-segmental features
Recognition of spontaneous emotions by speech within automotive environment
Frame vs. turn-level: emotion recognition from speech considering static and dynamic processing
Timing levels in segment-based speech emotion recognition
Optimization of discriminative models for speech and handwriting recognition
Hidden Markov model-based speech emotion recognition
Static and dynamic modelling for the recognition of non-verbal vocalisations in conversational speech
Recognition of interest in human conversational speech
Combining speech recognition and acoustic word emotion models for robust text-independent emotion recognition
Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture
Combining long short-term memory and dynamic Bayesian networks for incremental emotion-sensitive artificial listening
Speech recognition in noisy environments using a switching linear dynamic model for feature enhancement
Recognising interest in conversational speech - comparing bag of frames and supra-segmental features
Recognition of spontaneous emotions by speech within automotive environment
Frame vs. turn-level: emotion recognition from speech considering static and dynamic processing
Timing levels in segment-based speech emotion recognition
Optimization of discriminative models for speech and handwriting recognition
Hidden Markov model-based speech emotion recognition
Static and dynamic modelling for the recognition of non-verbal vocalisations in conversational speech
Recognition of interest in human conversational speech
Combining speech recognition and acoustic word emotion models for robust text-independent emotion recognition
Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture
Combining long short-term memory and dynamic Bayesian networks for incremental emotion-sensitive artificial listening
Speech recognition in noisy environments using a switching linear dynamic model for feature enhancement
Recognising interest in conversational speech - comparing bag of frames and supra-segmental features
Recognition of spontaneous emotions by speech within automotive environment
Frame vs. turn-level: emotion recognition from speech considering static and dynamic processing
Timing levels in segment-based speech emotion recognition
Optimization of discriminative models for speech and handwriting recognition
Hidden Markov model-based speech emotion recognition
Static and dynamic modelling for the recognition of non-verbal vocalisations in conversational speech
Recognition of interest in human conversational speech
Combining speech recognition and acoustic word emotion models for robust text-independent emotion recognition
Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture
Combining long short-term memory and dynamic Bayesian networks for incremental emotion-sensitive artificial listening