Characterising the Speech of People with Mental Illness
November 9, 2017
This year, my contribution to Psychonomics 2017 is in collaboration with Kristin Nicodemus of the University of Edinburgh and Alex Cohen of Louisiana State University.
My main research interest is to support people with chronic conditions. I also have a longstanding interest in the complex information that people convey in their speech and language – both intentionally, a signals to others, and unintentionally, as an expression of their socialisation, their anatomy, their physiology, and their current health.
This piece of work brings both together. Alex Cohen has a large collection of speech samples (17K+) from people with varying mental health conditions, which were analysed using a standard set of 88 features that have been used to describe aspects of speech and voice that are relevant to expressing emotion, the Geneva Minimalistic Acoustic Parameter Set (GeMAPS). GeMAPS is attractive, because it represents a consensus from the top researchers and practitioners in the field, and it comes with open source software for extracting those parameters from the speech signal.
GeMAPS contains 88 features and has been used mainly for classification of large data sets, but for smaller studies, it can be tricky to manage. Using principal component analysis in R, we reduced GeMAPS to a smaller set of features that are relatively easy to interpret from a phonetic point of view.
Using this reduced feature set, we’ve been able to identify distinct acoustic traces in the speech of people who have a history of depression and the speech of people who have a history of psychosis. These traces on their own are not enough to spot or diagnose mental illness or a history thereof, because they can be caused by many different factors. Instead, they reflect small, subtle changes, one of many traces that a person’s mental health leaves in their behaviour.
PDF of the poster:
Psychonomics 2017 PDF
Cohen A, Elvevåg B. 2014. Automated computerized analysis of speech disturbances in psychiatric disorders. Curr Opin Psychiatry 27:203–209
Cummins N, Scherer S, Krajewski J, Schnieder S, Epps J, Quatieri TF. 2015. A review of depression and suicide risk assessment using speech analysis. Speech Commun 71:10–49
Elvevåg B, Cohen AS, Wolters MK, Whalley HC, Gountouna V-E, Kuznetsova KA, Watson AR, Nicodemus KK. 2016. An Examination of the Language Construct in NIMH’s Research Domain Criteria: Time for Reconceptualization!. Am J Med Genet Part B 171B:904–919
Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, Sanislow C, Wang P. 2010. Research domain criteria (RDoC): Toward a new classification framework for research on mental disorders. Am J Psychiatry 167:748–751.
RDoC web page: Cognitive Systems / Language
The data used in this study come from these studies:
Cohen, A. S., Dinzeo, T. J., Donovan, N. J., Brown, C. E., & Morrison, S. C. (2015). Vocal acoustic analysis as a biometric indicator of information processing: Implications for neurological and psychiatric disorders. Psychiatry Research, 226(1), 235–241. https://doi.org/10.1016/j.psychres.2014.12.054
Cohen, A. S., Mitchell, K. R., Docherty, N. M., & Horan, W. P. (2016). Vocal Expression in Schizophrenia: Less Than Meets the Ear. Journal of Abnormal Psychology, 125(2), 299–309. https://doi.org/10.1037/abn0000136
Cohen, A. S., Renshaw, T. L., Mitchell, K. R., & Kim, Y. (2016). A psychometric investigation of “macroscopic” speech measures for clinical and psychological science. Behavior Research Methods, 48(2), 475–486. https://doi.org/10.3758/s13428-015-0584-1
Pint of Science: Oh Data, Where Art Thou?
May 15, 2017
In this post, I provide some background on the health data talk I gave on May 15, 2017, at Pint of Science, Edinburgh. (Slides)
The central argument of the talk is that any data we collect about health and wellbeing have no meaning in themselves – they need to be interpreted in context. Take step counts, for example. Measuring step counts is a somewhat inexact science, because the signals picked up by the accelerometers in a phone or a dedicated pedometer or actigraph need to be converted into the metric of steps (Berendsen et al, 2014; Fulk et al, 2014). Rating threads about pedometers like the FitBit or Jawbone often contain disappointed comments about bad measurements (too many steps counted, too few steps counted, failure to detect stair climbing).
Step counts also need to be interpreted in the context of the person who is taking the steps. 6000 steps in a day is impressive for somebody who barely walks, but an indication of a lazy day for somebody who usually averages 10000 or more.
So, we need to bear two contexts in mind if we want to interpret objective data such as step counts, the context of measurement in which the data were acquired, and the context of the person who generated the data.
When estimating the probability p(cause | symptom) that somebody has a certain condition, such as depression, given the signs they exhibit, such as activity levels measured in step counts, it’s worth considering several related probabilities:
- p(symptom). The probability that somebody exhibits the symptom. If the symptom is very common, it’s unlikely to be a strong indicator for the cause, especially if it can have multiple causes. A classic example is the humble cough, which can be a sign of the common cold or an indicator of lung cancer.
- p(cause). The probability that the cause occurs. This is the old adage “When you hear hoofbeats, think horses, not zebras.” Unfortunately, rare diseases are more frequent than one might think.
- p(symptom | cause). When you look at the diagnostic criteria for most illnesses, you will often find a list of several symptoms, together with the qualification “if two or more of these indicators are present, then …”
Even worse, diseases commonly occur together (Mokraoui et al., 2016), and some of these may have overlapping symptoms.
So, what should we do when we read about yet another algorithm that can diagnose depression? First of all, every diagnosis, in particular when it comes from algorithms, should be treated as a working hypothesis. In fact, some diseases, such as dementia, can only be diagnosed with absolute certainty after a person has died and their brain has been autopsied (Toledo et al., 2013). Secondly, even if the measurements we take are objective and repeatable, we can only make sense of them in the context in which they were taken, which includes both the person and the (measurement) process.
What do you think – is objectivity possible? Am I too pessimistic?
Berendsen, B. A., Hendriks, M. R., Meijer, K., Plasqui, G., Schaper, N. C., & Savelberg, H. H. (2014). Which activity monitor to use? Validity, reproducibility and user friendliness of three activity monitors. BMC Public Health, 14(1), 749. https://doi.org/10.1186/1471-2458-14-749
Fulk, G. D., Combs, S. A., Danks, K. A., Nirider, C. D., Raja, B., & Reisman, D. S. (2014). Accuracy of 2 activity monitors in detecting steps in people with stroke and traumatic brain injury. Physical Therapy, 94(2), 222–9. https://doi.org/10.2522/ptj.20120525
Mokraoui, N.-M., Haggerty, J., Almirall, J., & Fortin, M. (2016). Prevalence of self-reported multimorbidity in the general population and in primary care practices: a cross-sectional study. BMC Research Notes, 9(1), 314. https://doi.org/10.1186/s13104-016-2121-4
Toledo, J. B., Van Deerlin, V. M., Lee, E. B., Suh, E., Baek, Y., Robinson, J. L., … Trojanowski, J. Q. (2013). A platform for discovery: The University of Pennsylvania Integrated Neurodegenerative Disease Biobank. Alzheimer’s & Dementia, null(null). https://doi.org/10.1016/j.jalz.2013.06.003
What Big Data Can Tell You About Useful mHealth
June 23, 2016
perience work with clients around the world. If we want to harness the promise of Big Data to draw conclusions about the usability and usefulness of an mHealth app, Dray’s Law is an ideal starting point, because it givesus the fundamental variable we need to measure – how often people use an app.