Modeling drivers' speech under stress
Speech Communication - Special issue on speech and emotion
Hidden Markov model-based speech emotion recognition
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Fear-type emotion recognition for future audio-based surveillance systems
Speech Communication
Affect and Emotion in Human-Computer Interaction
Speech Emotion Recognition Using Spectral Entropy
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part II
Computer Speech and Language
Spoken emotion recognition using hierarchical classifiers
Computer Speech and Language
Relevance vector machine based speech emotion recognition
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Using emotional classification model for travel information system
International Journal of Computational Science and Engineering
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In this work we elaborate the use of hidden Markov models (HMMs) for speech emotion recognition as a dynamic alternative to static modelling approaches. Since previous work on this field does not yet define a clear line which HMM design should be prioritised for this task, we run a systematic analysis of different HMM configurations. Furthermore, experiments are carried out on an acted and a spontaneous emotions corpus, since little is known about the suitability of HMMs for spontaneous speech. Additionally, we consider two different segmentation levels, namely words and utterances. Results are compared with the outcome of a support vector machine classifier trained on global statistics features. While for both databases similar performance was observed on utterance level, the HMM-based approach outperformed static classification on word level. However, setting up general guidelines which kind of models are best suited appeared to be rather difficult.