Combining frame and turn-level information for robust recognition of emotions within speech
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
In: INTERSPEECH 2007 - 8th Annual Conference of the International Speech Communication Association, Antwerp, Belgium, August 27-31, 2007, S. 2249-2252
- Klassifikation
-
Informatik
- Ereignis
-
Veröffentlichung
- (wo)
-
Augsburg
- (wer)
-
Universität Augsburg, ISCA Archive
- (wann)
-
2007
- Urheber
- URN
-
urn:nbn:de:bvb:384-opus4-767172
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
25.03.2025, 13:51 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Vlasenko, Bogdan
- Schuller, Björn
- Wendemuth, Andreas
- Rigoll, Gerhard
- Universität Augsburg, ISCA Archive
Entstanden
- 2007
Ähnliche Objekte (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