| 1 |
Identifying signals associated with psychiatric illness utilizing language and images posted to Facebook
|
|
|
|
In: NPJ Schizophr (2020)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Detection of acute 3,4-methylenedioxymethamphetamine (MDMA) effects across protocols using automated natural language processing
|
|
|
|
BASE
|
|
Show details
|
|
|
| 3 |
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.
|
|
Keyword:
Article
|
|
URL: http://www.ncbi.nlm.nih.gov/pubmed/32095118
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7039659/
https://doi.org/10.1109/icassp.2019.8682691
|
|
BASE
|
|
Hide details
|
|
|
|
|
