8th Annual Conference of the International Speech Communication Association

Antwerp, Belgium
August 27-31, 2007

Direct Acoustic Feature Using Iterative EM Algorithm and Spectral Energy for Classifying Suicidal Speech

T. Yingthawornsuk, H. Kaymaz Keskinpala, D. M. Wilkes, R. G. Shiavi, R. M. Salomon

Vanderbilt University, USA

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.

Full Paper

Bibliographic reference.  Yingthawornsuk, T. / Keskinpala, H. Kaymaz / Wilkes, D. M. / Shiavi, R. G. / Salomon, R. M. (2007): "Direct acoustic feature using iterative EM algorithm and spectral energy for classifying suicidal speech", In INTERSPEECH-2007, 766-769.