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Vogt, Thurid: Real-time automatic emotion recognition from speech. 2010
Inhalt
Introduction
Research questions
Outline of the thesis
Basics of emotions in speech
A psychological view on emotions: Models of emotions
The expression of emotion
Language and speech
Extra-linguistic modalities
Automatic emotion recognition from speech
Overview of a statistical speech emotion recognition system
Feature calculation
Databases with emotional speech
Types of emotion databases
Labelling emotional databases
Summary
Approaches to speech emotion recognition
Emotion units
Features
Global statistics features
Short-term features
Non-acoustic features
Feature selection
Classification
Static classification
Dynamic classification
Trends in speech emotion classification
Speaker types, languages and target classes
Real-time speech emotion recognition
Applications for speech emotion recognition
Multimodal emotion recognition
Conclusion
Databases and methods for speech emotion recognition
Databases
Berlin Database of Emotional Speech
FAU Aibo Emotion Corpus
SmartKom Corpus
Audio segmentation
Features
Feature extraction
Feature selection
Classification
Support Vector Machines
Naïve Bayes
Summary
Experimental results
Evaluation measures
Emotion units
Feature evaluation
Individual feature ranking
Intra-feature type comparisons
Inter-feature type comparison
Reducing the number of statistical measures
Automatically selected features
Feature subsets
Classifiers
Conclusion
EmoVoice — Real-time speech emotion recognition
Architecture
Data acquisition
Sample applications and prototypes
Conversational applications
Artistic applications
Emotion expressions under realistic conditions
Evaluation studies
Barthoc jr.
E-Tree
EmoEmma
Conclusion
Multimodal emotion recognition
Gender information
Combined gender and emotion recognition
Relevant features
Results and discussion
Conclusion
Biosignals
Experiment for data collection
Recognition of emotions from speech and biosignals
Results and discussion
Conclusion
Linguistic information
The SAL database
Feature sets
Results and discussion
Conclusion
Facial expressions
The DaFEx database
Recognition of emotions from facial expressions and speech
Results and discussion
Conclusion
Conclusion
Final conclusion and outlook
Bibliography