Abstract
There is potential to leverage lexical and acoustic features as predictors of clinical ratings used to measure thought disorder and negative symptoms of schizophrenia. In this paper, segments of speech from individuals identified as having schizophrenia were extracted from publicly available educational videos and used accordingly. We explored correlations and fit a LASSO regression model to predict individual clinical measures from the language and acoustic features. We were able to predict poverty of speech, perseveration, and latency with R\(^{2}\) values of 0.60, 0.54, and 0.62 on a held-out test set. We note and discuss the important predicting features as well as synchronicities between correlation and prediction results.
The first two authors contributed equally to this work.
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Notes
- 1.
Some videos included multiple interviewed subjects. The list of videos from which samples were derived can be found here: https://pastebin.com/fz1smhzn.
- 2.
WebTrans is an online version of XTrans developed by the Linguistic Data Consortium, see https://www.ldc.upenn.edu/language-resources/tools/xtrans.
References
Alhanai, T.: acousticfeatures-fhs. https://github.com/talhanai/acousticfeatures-fhs (2017)
Andreasen, N.: Scale for the assessment of negative symptoms (sans)*. Brit. J. Psychiatr. 155(S7), 53–58 (1989). https://doi.org/10.1192/S0007125000291502
Andreasen, N., Flaum, M., Swayze, V., Tyrrell, G., Arndt, S.: Positive and negative symptoms in schizophrenia. Archives General Psychiatry 47, 615–621 (1990)
Andreasen, N.: Scale for the assessment of thought, language, and communication (TLC). Schizophrenia Bulletin 12(3), 473–482 (1986). https://doi.org/10.1093/schbul/12.3.473
Cohen, A.S., Schwartz, E., Le, T.P., Cowan, T., Kirkpatrick, B., Raugh, I.M., Strauss, G.P.: Digital phenotyping of negative symptoms: the relationship to clinician ratings. Schizophrenia Bulletin 1–10 (2020). https://doi.org/10.1093/schbul/sbaa065
Dombrowski, M., McCleery, A., Gregory, S.W., Docherty, N.M.: Stress reactivity of emotional and verbal speech content in Schizophrenia. J. Nerv. Mental Disease 202(8) (2014). https://doi.org/10.1097/NMD.0000000000000169
Elvevåg, B., Foltz, P.W., Weinberger, D.R., Goldberg, T.E.: Quantifying incoherence in speech: an automated methodology and novel application to schizophrenia. Schizophrenia Res. 93(1–3), 304–316 (2007). https://doi.org/10.1016/j.schres.2007.03.001
Eyben, F., Wöllmer, M., Schuller, B.: Opensmile: the munich versatile and fast open-source audio feature extractor. In: Proceedings of the International Conference on Multimedia—MM ’10. ACM Press, Firenze, Italy (2010). https://doi.org/10.1145/1873951.1874246
Günther, F., Dudschig, C., Kaup, B.: LSAfun—an R package for computations based on latent semantic analysis. Beh. Res. Methods 47(4), 930–944 (2015). https://doi.org/10.3758/s13428-014-0529-0
Iter, D., Yoon, J., Jurafsky, D.: Automatic detection of incoherent speech for diagnosing schizophrenia. In: Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pp. 136–146 (2018)
Joseph, B., Narayanaswamy, J.C.: Insight in schizophrenia: relationship to positive, negative and neurocognitive dimensions. Indian J. Psychol. Med. 37(1) (2015). https://doi.org/10.4103/0253-7176.15079
Kuperberg, G.R.: Language in schizophrenia part 1: an introduction. Lang. Linguistics Compass 4(8) (2010). https://doi.org/10.1111/j.1749-818X.2010.00216.x
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013)
Moschopoulos, N., Nimatoudis, I., Kaprinis, S., Sidiras, C., Iliadou, V.: Auditory processing disorder may be present in schizophrenia and it is highly correlated with formal thought disorder. Psychiatry Res. 291 (2020). https://doi.org/10.1016/j.psychres.2020.113222
NIMH: Schizophrenia (2018), https://www.nimh.nih.gov/health/statistics/schizophrenia.shtml
Nour, M.M., Nour, M.H., Tsatalou, O.M., Barrera, A.: Schizophrenia on youtube. Psychiatric Serv. 68(1), 70–74 (2017). https://doi.org/10.1176/appi.ps.201500541
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Püschel, J., Stassen, H., Bomben, G., Scharfetter, C., Hell, D.: Speaking behavior and speech sound characteristics in acute schizophrenia. J. Psychiatric Res. 32(2) (1998). https://doi.org/10.1016/S0022-3956(98)00046-6
SARDAA: The Truth About Schizophrenia (2010), https://www.slideshare.net/SARDAA/the-truth-about-schizophrenia
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Krell, R. et al. (2022). Lexical and Acoustic Correlates of Clinical Speech Disturbance in Schizophrenia. In: Shaban-Nejad, A., Michalowski, M., Bianco, S. (eds) AI for Disease Surveillance and Pandemic Intelligence. W3PHAI 2021. Studies in Computational Intelligence, vol 1013. Springer, Cham. https://doi.org/10.1007/978-3-030-93080-6_3
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