Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Classification of speech under stress using target driven features
Speech Communication - Special issue on speech under stress
Hidden Markov model-based speech emotion recognition
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
A finite element model of fluid flow in the vocal tract
Computer Speech and Language
A prototype for a conversational companion for reminiscing about images
Computer Speech and Language
Detection of time-pressure induced stress in speech via acoustic indicators
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Gender-dependent emotion recognition based on HMMs and SPHMMs
International Journal of Speech Technology
International Journal of Speech Technology
Hi-index | 0.00 |
The determination of an emotional state through speech increases the amount of information associated with a speaker. It is therefore important to be able to detect and identify a speaker's emotional state or state of stress. Various techniques are used in the literature to classify emotional/stressed states on the basis of speech, often using different speech feature vectors at the same time. This study proposes a new feature vector that will allow better classification of emotional/stressed states. The components of the feature vector are obtained from a feature subset selection procedure based on genetic algorithms. A good discrimination between neutral, angry, loud and Lombard states for the simulated domain of the Speech Under Simulated and Actual Stress (SUSAS) database and between neutral and stressed states for the actual domain of the SUSAS database is obtained.