The digital therapist From: ECN - 11/05/2014 Imagine this scenario: You've been feeling persistently blue lately, so you pull out your phone. Instead of asking Siri to tell you a joke, though, you open an app that records you simply talking about your day. A few hours later, your therapist sends you a message asking if you'd like to meet. A program like this one that analyzes your speech and uses it to gain information about your mental health could soon be feasible, thanks in part to research from the University of Maryland showing that certain vocal features change as patients' feelings of depression worsen. When patients' feelings of depression were worst, their speech tended to be breathier and slower. The team also found increases in jitter and shimmer, two measures of acoustic disturbance that measure the frequency and amplitude variation of the sound, respectively. Speech high in jitter and shimmer tends to sound hoarse or rough. Sometimes, patients might not recognize or be willing to admit that they are depressed. By receiving regular feedback based on acoustical and other measurements, they might learn to self-monitor their mental states and recognize when they should seek help. The technology could also promote communication between therapists and patients, allowing for continuous, responsive care in addition to regular in-person appointments. Read the entire article at: http://www.ecnmag.com/news/2014/11/digital-therapist http://www.eurekalert.org/pub_releases/2014-10/asoa-tdt102314.php --- Acoustical Society of America Poster Abstract In this paper we are investigating the effects of depression on speech. The motivation comes from the fact that neuro-physiological changes associated with depression affect motor coordination and can disrupt the articulatory precision in speech. We use the database collected by Mundt et al. (J Neurolinguistics. Jan 2007; 20(1): 50-64.) in which 35 subjects were treated over a 6 week period and study how the changes in mental state are manifest in certain acoustic properties that correlate with the Hamilton Depression Rating Scale (HAM-D) which is a clinical assessment score. We look at features such as the modulation frequencies, aperiodic energy during voiced speech, vocal fold jitter and shimmer and other cues that are related to articulatory precision. These measures will be discussed in detail. Saurabh Sahu ssahu89@umd.edu Carol Espy-Wilson A.V. Williams Bldg. University of Maryland College Park, MD 20742 espy@isr.umd.edu