Abstract
People with schizophrenia generally have different speech disorders, such as tangentiality, dissociation of thought, or perseveration. These disorders can be classified using the Thought and Language Disorder (TALD) scale, which measures the intensity of the different disorders based on the presence of particular characteristics of the speech. At present, the current practice to measure speech disorders through the TALD scale involves specialized clinicians who administer specific interviews to assess the degree of TALD’s sub-scales with no technological support. Conducting the interviews is a very time consuming activity and an automatic support for the interview administration and the identification of the language disorders may be useful. In this paper, we propose an approach named RoboTald in which a robot is adopted for formulating questions to patients by following the TALD guidelines and to record their answers. The audio files are then automatically transcripted and inputted to a deep-learning-based system aiming at assessing the speech disorders basing on the TALD classification. Faced with the need to improve and speed up the classification and quantification of disorders we adopt NLP models for analyzing the speech characteristics. In particular, the robot dialogues with the patient and collects her speech. Starting from the transcription of patient’s speech, TALD Item measurements are computed by Transformer models, able to recognize the semantic meaning of sentences and to understand the similarity between two inputs using the “Cosine Similarity” technique. This technique identifies the semantic distance between two inputs based on the features extracted from the text provided to the Transformer. The distances are then used to evaluate the elements of the scale by analyzing how far the various sentences deviate from the initial question or how far apart they are from each other. In this preliminary work we focus our attention on two elements of the TALD scale: Tangentiality and Derailment.
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Notes
- 1.
https://www.sbert.net/docs/pretrained_models.html, accessed on 22 June 2023.
References
Andreasen, N.C.: Scale for the assessment of thought, language, and communication (TLC). Schizophr. Bull. 12(3), 473 (1986)
Bedi, G., et al.: Automated analysis of free speech predicts psychosis onset in high-risk youths. npj Schizophr. 1(1), 1–7 (2015)
Bleuler, E.: Dementia praecox or the group of schizophrenias (1950)
Brugger, S.P., Howes, O.D.: Heterogeneity and homogeneity of regional brain structure in schizophrenia: a meta-analysis. JAMA Psychiat. 74(11), 1104–1111 (2017)
Chang, Y.L., Luo, D.H., Huang, T.R., Goh, J.O., Yeh, S.L., Fu, L.C.: Identifying mild cognitive impairment by using human-robot interactions. J. Alzheimers Dis. 85(3), 1129–1142 (2022)
Clark, K., Luong, M.T., Le, Q.V., Manning, C.D.: ELECTRA: pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555 (2020)
Corcoran, C.M., et al.: Language as a biomarker for psychosis: a natural language processing approach. Schizophr. Res. 226, 158–166 (2020)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423. https://aclanthology.org/N19-1423
Elvevåg, B., Foltz, P.W., Weinberger, D.R., Goldberg, T.E.: Quantifying incoherence in speech: an automated methodology and novel application to schizophrenia. Schizophr. Res. 93(1–3), 304–316 (2007)
Huang, Y.J., et al.: Assessing schizophrenia patients through linguistic and acoustic features using deep learning techniques. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 947–956 (2022)
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)
Kay, S.R., Fiszbein, A., Opler, L.A.: The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr. Bull. 13(2), 261–276 (1987)
Kircher, T., et al.: A rating scale for the assessment of objective and subjective formal thought and language disorder (TALD). Schizophr. Res. 160(1–3), 216–221 (2014). https://doi.org/10.1016/j.schres.2014.10.024. https://www.sciencedirect.com/science/article/pii/S0920996414005933
Landauer, T.K., Foltz, P.W., Laham, D.: An introduction to latent semantic analysis. Discourse Process. 25(2–3), 259–284 (1998)
Liu, A.T., Li, S.W., Lee, H.Y.: TERA: self-supervised learning of transformer encoder representation for speech. IEEE/ACM Trans. Audio Speech Lang. Process. 29, 2351–2366 (2021)
World Health Organization: Schizophrenia. https://www.who.int/news-room/fact-sheets/detail/schizophrenia
Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. arXiv preprint arXiv:1908.10084 (2019)
Rossi, S., et al.: The role of personality factors and empathy in the acceptance and performance of a social robot for psychometric evaluations. Robotics 9(2), 39 (2020). https://doi.org/10.3390/robotics9020039. https://www.mdpi.com/2218-6581/9/2/39
Rossi, S., Santangelo, G., Staffa, M., Varrasi, S., Conti, D., Di Nuovo, A.: Psychometric evaluation supported by a social robot: Personality factors and technology acceptance. In: 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 802–807. IEEE Press (2018). https://doi.org/10.1109/ROMAN.2018.8525838
Rossi, S., et al.: Robot head movements and human effort in the evaluation of tracking performance. In: RO-MAN, pp. 791–796. IEEE (2015). http://dblp.uni-trier.de/db/conf/ro-man/ro-man2015.html#RossiSGGRTV15
Rossi, S., Staffa, M., Tamburro, A.: Socially assistive robot for providing recommendations: comparing a humanoid robot with a mobile application. Int. J. Soc. Robot. 10(2), 265–278 (2018). https://doi.org/10.1007/s12369-018-0469-4. A correction to this article is available https://doi.org/10.1007/s12369-018-0489-0
Tang, S.X., et al.: Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders. npj Schizophr. 7(1), 25 (2021)
Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: evaluating text generation with BERT. arXiv preprint arXiv:1904.09675 (2019)
Acknowledgement
We acknowledge financial support from the Research Projects of Significant National Interest (PRIN) 2022 PNRR, project n. D53D23017290001 entitled “Supporting schizophrenia PatiEnts Care wiTh aRtificiAl intelligence (SPECTRA)”.
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Amaro, I., Francese, R., Tortora, G., Tucci, C., D’Errico, L., Staffa, M. (2023). Supporting Schizophrenia PatiEnts’ Care wiTh Robotics and Artificial Intelligence. In: Gao, Q., Zhou, J., Duffy, V.G., Antona, M., Stephanidis, C. (eds) HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14055. Springer, Cham. https://doi.org/10.1007/978-3-031-48041-6_32
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