A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Rules for the generation of ToBI-based American English intonation
Speech Communication
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Describing the emotional states that are expressed in speech
Speech Communication - Special issue on speech and emotion
A corpus-based speech synthesis system with emotion
Speech Communication - Special issue on speech and emotion
The role of voice quality in communicating emotion, mood and attitude
Speech Communication - Special issue on speech and emotion
Emotions, speech and the ASR framework
Speech Communication - Special issue on speech and emotion
Vocal communication of emotion: a review of research paradigms
Speech Communication - Special issue on speech and emotion
Emotional Speech as an Effective Interface for People with Special Needs
APCHI '98 Proceedings of the Third Asian Pacific Computer and Human Interaction
The Image Foresting Transform: Theory, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Links between perceptrons, MLPs and SVMs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Hidden Markov model-based speech emotion recognition
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Methods for stress classification: nonlinear TEO and linear speech based features
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 04
Discrete-time speech signal processing: principles and practice
Discrete-time speech signal processing: principles and practice
Object delineation by κ-connected components
EURASIP Journal on Advances in Signal Processing
A discrete approach for supervised pattern recognition
IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
A novel source analysis method by matching spectral characters of LF model with STRAIGHT spectrum
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Automatic Prosodic Event Detection Using Acoustic, Lexical, and Syntactic Evidence
IEEE Transactions on Audio, Speech, and Language Processing
Spoken emotion recognition using hierarchical classifiers
Computer Speech and Language
Spoken emotion recognition using glottal symmetry
EURASIP Journal on Advances in Signal Processing - Special issue on emotion and mental state recognition from speech
Efficient supervised optimum-path forest classification for large datasets
Pattern Recognition
Emotional speech classification using hidden conditional random fields
Proceedings of the Second Symposium on Information and Communication Technology
Emotion recognition from speech: a review
International Journal of Speech Technology
Characterization and recognition of emotions from speech using excitation source information
International Journal of Speech Technology
Dimensionality reduction-based spoken emotion recognition
Multimedia Tools and Applications
Phonetic feature extraction for context-sensitive glottal source processing
Speech Communication
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A new method for the recognition of spoken emotions is presented based on features of the glottal airflow signal. Its effectiveness is tested on the new optimum path classifier (OPF) as well as on six other previously established classification methods that included the Gaussian mixture model (GMM), support vector machine (SVM), artificial neural networks - multi layer perceptron (ANN-MLP), k-nearest neighbor rule (k-NN), Bayesian classifier (BC) and the C4.5 decision tree. The speech database used in this work was collected in an anechoic environment with ten speakers (5M and 5F) each speaking ten sentences in four different emotions: Happy, Angry, Sad, and Neutral. The glottal waveform was extracted from fluent speech via inverse filtering. The investigated features included the glottal symmetry and MFCC vectors of various lengths both for the glottal and the corresponding speech signal. Experimental results indicate that best performance is obtained for the glottal-only features with SVM and OPF generally providing the highest recognition rates, while for GMM or the combination of glottal and speech features performance was relatively inferior. For this text dependent, multi speaker task the top performing classifiers achieved perfect recognition rates for the case of 6th order glottal MFCCs.