Fundamentals of speech recognition
Fundamentals of speech recognition
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
2005 Special Issue: Emotion recognition in human-computer interaction
Neural Networks - Special issue: Emotion and brain
Real-Time Emotion Recognition from Speech Using Echo State Networks
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
RASTA-PLP speech analysis technique
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
Multimodal emotion classification in naturalistic user behavior
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: towards mobile and intelligent interaction environments - Volume Part III
Multiple classifier systems for the recogonition of human emotions
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Feature Analysis and Evaluation for Automatic Emotion Identification in Speech
IEEE Transactions on Multimedia
Computer Speech and Language
On instance selection in audio based emotion recognition
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Emotion recognition in the wild challenge 2013
Proceedings of the 15th ACM on International conference on multimodal interaction
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Systems for the recognition of psychological characteristics such as the emotional state in real world scenarios have to deal with several difficulties. Amongst those are unconstrained environments and uncertainties in one or several input channels. However a more crucial aspect is the content of the data itself. Psychological states are highly person-dependent and often even humans are not able to determine the correct state a person is in. A successful recognition system thus has to deal with data, that is not very discriminative and often simply misleading. In order to succeed, a critical view on features and decisions is essential to select only the most valuable ones. This work presents a comparison of a common multi classifier system approach based on state of the art features and a modified forward backward feature selection algorithm with a long term stopping criteria. The second approach takes also features of the voice quality family into account. Both approaches are based on the audio modality only. The dataset used in the challenge is an in between dataset of real world datasets which are still very hard to handle and over acted datasets which were famous in the past and today are well understood.