Abstract
Schizophrenia is a chronic mental disorder that contributes to poor function and quality of life. We are aiming to design objective assessment tools of schizophrenia. In earlier work, we investigated non-verbal quantitative cues for this purpose. In this paper, we explore linguistic cues, extracted from interviews with patients with schizophrenia and healthy control subjects, conducted by trained psychologists. Specifically, we analyzed the interviews of 47 patients and 24 healthy age-matched control subjects. We applied automated speech recognition and linguistic tools to capture the linguistic categories of emotional and psychological states. Based on those linguistic categories, we applied a binary classifier to distinguish patients from matched control subjects, leading to a classification accuracy of about 86% (by leave-one-out cross-validation); this result seems to suggest that patients with schizophrenia tend to talk about different topics and use different words. We provided an in-depth discussion of the most salient lexical features, which may provide some insights into the linguistic alterations in patients.
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References
Demily C, Franck N (2008) Cognitive remediation: a promising tool for the treatment of schizophrenia. Expert Rev Neurother 8(7):1029–1036
Covington MA, He C, Brown C, Nai L, McClain JT, Fjordbak BS, Semple J, Brown J (2005) Schizophrenia and the structure of language: the linguist’s view. Schizophr Res 77(1):85–98
Andreasen NC (1979) Thought, language, and communication disorders: I. Clinical assessment, definition of terms, and evaluation of their reliability. Arch Gen Psychiatry 36(12):1315–1321
Slatcher RB, Trentacosta CJ (2012) Influences of parent and child negative emotionality on young children’s everyday behaviors. Emotion 12(5):932
Rude S, Gortner EM, Pennebaker J (2004) Language use of depressed and depression-vulnerable college students. Cogn & Emot 18(8):1121–33
Losh M, Gordon PC (2014) Quantifying narrative ability in autism spectrum disorder: a computational linguistic analysis of narrative coherence. J Autism Dev Disord 44(12):3016–25
Jackson RG, Patel R, Jayatilleke N, Kolliakou A, Ball M, Gorrell G, Roberts A, Dobson RJ, Stewart R (2017) Natural language processing to extract symptoms of severe mental illness from clinical text: the clinical record interactive search comprehensive data extraction (CRIS-CODE) project. BMJ Open 7(1):e012012
Nature Publishing Group (2018) Thinking big in mental health. Nat Med 24(1):1
Tausczik YR, Pennebaker JW (2010) The psychological meaning of words: LIWC and computerized text analysis methods. J Lang Soc Psychol 29(1):24–54
Pennebaker JW, Boyd RL, Jordan K, Blackburn K (2015) The development and psychometric properties of LIWC2015
St-Hilaire A, Cohen AS, Docherty NM (2008) Emotion word use in the conversational speech of schizophrenia patients. Cogn Neuropsychiatry 13(4):343–56
Junghaenel DU, Smyth JM, Santner L (2008) Linguistic dimensions of psychopathology: a quantitative analysis. J Soc Clin Psychol 27(1):36–55
Deutsch-Link S (2016) Language in schizophrenia: what we can learn from quantitative text analysis
Minor KS, Bonfils KA, Luther L, Firmin RL, Kukla M, MacLain VR, Buck B, Lysaker PH, Salyers MP (2015) Lexical analysis in schizophrenia: how emotion and social word use informs our understanding of clinical presentation. J Psychiatr Res 31(64):74–8
Hong K, Kohler CG, March ME, Parker AA, Nenkova A (2012) Lexical differences in autobiographical narratives from schizophrenic patients and healthy controls. In: Proceedings of the 2012 Joint conference on empirical methods in natural language processing and computational natural language learning. Association for Computational Linguistics pp 37-47
Tahir Y, Chakraborty D, Dauwels J, Thalmann N, Thalmann D, Lee J (2016) Non-verbal speech analysis of interviews with schizophrenic patients. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP) 2016 Mar 20. IEEE pp 5810–5814
Chakraborty D, Tahir Y, Yang Z, Maszczyk T, Dauwels J, Thalmann D, Thalmann NM, Tan BL, Lee J (2017) Assessment and prediction of negative symptoms of schizophrenia from RGB+ D movement signals. In: 2017 IEEE 19th international workshop on multimedia signal processing (MMSP). IEEE pp 1-6
Lange P, Suendermann-Oeft D (2014) Tuning sphinx to outperform googles speech recognition API. In: Proceedings of the ESSV 2014, conference on electronic speech signal processing, pp 1-10
Hinton G, Deng L, Yu D, Dahl GE, Mohamed AR, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath TN, Kingsbury B (2012) Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process Mag 29(6):82–97
Green MF, Penn DL, Bentall R, Carpenter WT, Gaebel W, Gur RC, Kring AM, Park S, Silverstein SM, Heinssen R (2008) Social cognition in schizophrenia: an NIMH workshop on definitions, assessment, and research opportunities. Schizophr Bull 34(6):1211–20
Kpuska V, Bohouta G (2017) Comparing speech recognition systems (Microsoft API, Google API and CMU Sphinx). Int J Eng Res Appl 7:20–24
Witten IH, Frank E, Trigg LE, Hall MA, Holmes G, Cunningham SJ (1999) Weka: practical machine learning tools and techniques with Java implementations
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. ACM SIGKDD Explor Newsl 11(1):10–8
Frank E, Hall M, Holmes G, Kirkby R, Pfahringer B, Witten IH, Trigg L (2009) Weka-a machine learning workbench for data mining. In: data mining and knowledge discovery handbook 2009. Springer, Boston, MA pp 1269–1277
Howes C, Purver M, McCabe R (2013) Using conversation topics for predicting therapy outcomes in schizophrenia. Biomed Inform Insights 6(Suppl 1):39
Penn DL, Corrigan PW, Bentall RP, Racenstein J, Newman L (1997) Social cognition in schizophrenia. Psychol Bull 121(1):114
Savla GN, Vella L, Armstrong CC, Penn DL, Twamley EW (2012) Deficits in domains of social cognition in schizophrenia: a meta-analysis of the empirical evidence. Schizophr Bull 39(5):979–92
Hfner H (2003) Gender differences in schizophrenia. Psychoneuroendocrinology 30(28):17–54
Ochoa S, Usall J, Cobo J, Labad X, Kulkarni J (2012) Gender differences in schizophrenia and first-episode psychosis: a comprehensive literature review. Schizophr Res Treat 8:2012
Diekman AB, Goodfriend W (2006) Rolling with the changes: A role congruity perspective on gender norms. Psychol Women Q 30(4):369–83
Chen FP, Greenberg JS (2004) A positive aspect of caregiving: The influence of social support on caregiving gains for family members of relatives with schizophrenia. Community Ment Health J 40(5):423–35
Acknowledgements
This study was funded by the Singapore Ministry of Health National Medical Research Council Center Grant awarded to the Institute of Mental Health Singapore (NMRC/CG /004/2013) and by NITHM grant M4081187.E30. This research is also supported in part by the Being Together Centre, a collaboration between Nanyang Technological University (NTU) Singapore and University of North Carolina (UNC) at Chapel Hill. The Being Together Centre is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its International Research Centres in Singapore Funding Initiative. Besides, this project is funded in part by the RRIS Rehabilitation Research Grant RRG2/16009. The authors also acknowledge support from the Interdisciplinary Graduate School at NTU.
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Xu, S. et al. (2019). Automated Lexical Analysis of Interviews with Individuals with Schizophrenia. In: D'Haro, L., Banchs, R., Li, H. (eds) 9th International Workshop on Spoken Dialogue System Technology. Lecture Notes in Electrical Engineering, vol 579. Springer, Singapore. https://doi.org/10.1007/978-981-13-9443-0_16
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DOI: https://doi.org/10.1007/978-981-13-9443-0_16
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