@inproceedings{d581999019284d32af40efbb7730ca67,
title = "Speech Markers for Clinical Assessment of Cocaine Users",
abstract = "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.",
keywords = "abstinence, acoustic, cocaine, drug addiction, semantic",
author = "Carla Agurto and Raquel Norel and Mary Pietrowicz and Muhammad Parvaz and Sivan Kinreich and Keren Bachi and Guillermo Cecchi and Goldstein, {Rita Z.}",
note = "Funding Information: We would like to thank to the participants of this study. The collection of data in this study was funded by NIH grant R01DA041528 (PI: Goldstein) Publisher Copyright: {\textcopyright} 2019 IEEE.; 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",
year = "2019",
month = may,
doi = "10.1109/ICASSP.2019.8682691",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6391--6394",
booktitle = "2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings",
address = "United States",
}