Research has shown that the voice itself contains important information about immediate psychological state and certain vocal parameters are capable of distinguishing speaking patterns of speech signal affected by emotional disturbances (i.e., clinical depression). In this study, the GMM based feature of the vocal tract system response and spectral energy have been studied and found to be a primary acoustic feature set for separating two groups of female patients carrying a diagnosis of depression and suicidal risk.
Cite as: Yingthawornsuk, T., Keskinpala, H.K., Wilkes, D.M., Shiavi, R.G., Salomon, R.M. (2007) Direct acoustic feature using iterative EM algorithm and spectral energy for classifying suicidal speech. Proc. Interspeech 2007, 766-769, doi: 10.21437/Interspeech.2007-144
@inproceedings{yingthawornsuk07_interspeech,
author={T. Yingthawornsuk and H. Kaymaz Keskinpala and D. M. Wilkes and R. G. Shiavi and R. M. Salomon},
title={{Direct acoustic feature using iterative EM algorithm and spectral energy for classifying suicidal speech}},
year=2007,
booktitle={Proc. Interspeech 2007},
pages={766--769},
doi={10.21437/Interspeech.2007-144}
}