Affective computing
What Size Test Set Gives Good Error Rate Estimates?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Prediction of Speech
Digital Filters and Signal Processing
Digital Filters and Signal Processing
Vocal communication of emotion: a review of research paradigms
Speech Communication - Special issue on speech and emotion
An introduction to variable and feature selection
The Journal of Machine Learning Research
Exploiting emotions to disambiguate dialogue acts
Proceedings of the 9th international conference on Intelligent user interfaces
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Emotive alert: HMM-based emotion detection in voicemail messages
Proceedings of the 10th international conference on Intelligent user interfaces
Improving automotive safety by pairing driver emotion and car voice emotion
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Affective multimodal human-computer interaction
Proceedings of the 13th annual ACM international conference on Multimedia
Mandarin Emotional Speech Recognition Based on SVM and NN
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Human computing and machine understanding of human behavior: a survey
Proceedings of the 8th international conference on Multimodal interfaces
Audiovisual recognition of spontaneous interest within conversations
Proceedings of the 9th international conference on Multimodal interfaces
A survey of affect recognition methods: audio, visual and spontaneous expressions
Proceedings of the 9th international conference on Multimodal interfaces
EmoVoice -- A Framework for Online Recognition of Emotions from Voice
PIT '08 Proceedings of the 4th IEEE tutorial and research workshop on Perception and Interactive Technologies for Speech-Based Systems: Perception in Multimodal Dialogue Systems
Expert Systems with Applications: An International Journal
Variational Gaussian Mixture Models for Speech Emotion Recognition
ICAPR '09 Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition
Comparing emotions using acoustics and human perceptual dimensions
CHI '09 Extended Abstracts on Human Factors in Computing Systems
Using affective avatars and rich multimedia content for education of children with autism
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Audio-Based Emotion Recognition in Judicial Domain: A Multilayer Support Vector Machines Approach
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Statistical Evaluation of Speech Features for Emotion Recognition
ICDT '09 Proceedings of the 2009 Fourth International Conference on Digital Telecommunications
Image and Vision Computing
An adaptive framework for acoustic monitoring of potential hazards
EURASIP Journal on Audio, Speech, and Music Processing
Emotion recognition from speech signals using new harmony features
Signal Processing
Class-level spectral features for emotion recognition
Speech Communication
A learning approach to hierarchical feature selection and aggregation for audio classification
Pattern Recognition Letters
Non-negative tensor factorization applied to music genre classification
IEEE Transactions on Audio, Speech, and Language Processing
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
IEEE Transactions on Affective Computing
Employing fujisaki's intonation model parameters for emotion recognition
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Analysis of Emotionally Salient Aspects of Fundamental Frequency for Emotion Detection
IEEE Transactions on Audio, Speech, and Language Processing
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In this paper, a psychologically-inspired binary cascade classification schema is proposed for speech emotion recognition. Performance is enhanced because commonly confused pairs of emotions are distinguishable from one another. Extracted features are related to statistics of pitch, formants, and energy contours, as well as spectrum, cepstrum, perceptual and temporal features, autocorrelation, MPEG-7 descriptors, Fujisaki's model parameters, voice quality, jitter, and shimmer. Selected features are fed as input to K nearest neighborhood classifier and to support vector machines. Two kernels are tested for the latter: linear and Gaussian radial basis function. The recently proposed speaker-independent experimental protocol is tested on the Berlin emotional speech database for each gender separately. The best emotion recognition accuracy, achieved by support vector machines with linear kernel, equals 87.7%, outperforming state-of-the-art approaches. Statistical analysis is first carried out with respect to the classifiers' error rates and then to evaluate the information expressed by the classifiers' confusion matrices.