Floating search methods in feature selection
Pattern Recognition Letters
Nonlinear time series analysis
Nonlinear time series analysis
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
A dimensional approach to emotion recognition of speech from movies
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Polish Emotional Speech Database --- Recording and Preliminary Validation
Cross-Modal Analysis of Speech, Gestures, Gaze and Facial Expressions
Characterization of healthy and pathological voice through measures based on nonlinear dynamics
IEEE Transactions on Audio, Speech, and Language Processing
Automatic recognition of speech emotion using long-term spectro-temporal features
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Detecting emotional state of a child in a conversational computer game
Computer Speech and Language
Automatic speech emotion recognition using modulation spectral features
Speech Communication
Anger recognition in speech using acoustic and linguistic cues
Speech Communication
Application of nonlinear dynamics characterization to emotional speech
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
On the Complexity of Finite Sequences
IEEE Transactions on Information Theory
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This paper proposes the application of complexity measures based on nonlinear dynamics for emotional speech characterization. Measures such as mutual information, dimension correlation, entropy correlation, Shannon entropy, Lempel-Ziv complexity and Hurst exponent are extracted from the samples of three databases of emotional speech. Then, statistics such as mean, standard deviation, skewness and kurtosis are applied on the extracted measures. Experiments were conducted on the Polish emotional speech database, on the Berlin emotional speech database and on the LCD emotional database for a three-class problem (neutral, fear and anger emotional states). A procedure for feature selection is proposed based on an affinity analysis of the features. This feature selection procedure is accomplished to select a reduced number of features over the Polish emotional database. Finally, the selected features are evaluated in the Berlin emotional speech database and in the LDC emotional database using a neural network classifier in order to assess the usefulness of the selected features. Global success rates of 72.28%, 75.4% and 80.75%, were obtained for the Polish emotional speech database, the Berlin emotional speech database and the LDC emotional speech database respectively.