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Improving speech-based PTSD detection via multi-view learning

We demonstrate that by applying multi-view learning algorithms one can usefully leverage highly informative, highcost, psychophysiological data collected in a laboratory setting, to improve PTSD screening in the field, where only less-informative, low-cost, speech data are available. Cost metrics reflect resource requirements as well as subject receptivity to data collection. The speech-based representation involves distress indicator extraction from automatic speech recognition output, and a compact holistic audio representation based on the i-vector method. A prototype PTSD screening system was developed that benefits from highly informative EEG data yet, in the field, only relies on subjects' spoken commentary in response to open ended questions. Such a system can deliver screening with significantly increased engagement, to a broader population, leading to earlier intervention and improved outcomes. Using a recent dataset collected for multi-modal computer-aided diagnosis of PTSD, we demonstrate that the proposed method significantly improves speech-based PTSD detection, without requiring costly and aversive procedures at deployment.