Erhan Bilal, Theodore Sakellaropoulos, et al.
Bioinformatics
One of the main foci of addiction research is the delineation of markers that track the propensity of relapse. Speech analysis can provide an unbiased assessment that can be deployed outside the lab, enabling objective measurements and relapse susceptibility tracking. This work is the first attempt to study unscripted speech markers in cocaine users. We analyzed 23 subjects performing two tasks: describing the positive consequences (PC) of abstinence and the negative consequences (NC) of using cocaine. We perform two main experiments: first, we analyzed whether acoustic and semantic features can infer clinical variables such as the Cocaine Selective Severity Assessment; then, we analyzed the main problem of interest: to see if these features are powerful enough to infer if the subjects remains abstinent. Our results show that speech features have potential to be used as a proxy to monitor cocaine users under treatment to recover from their addiction.
Erhan Bilal, Theodore Sakellaropoulos, et al.
Bioinformatics
David Haws, Xiaodong Cui
ICASSP 2019
Daniel S. Barron, Stephen Heisig, et al.
Computational Psychiatry
Carla Agurto, Pat Pataranutaporn, et al.
ICSC 2018