Abstract
Psychosis is a mental syndrome associated to loss of contact with reality which may arise in patients with different diseases, such as schizophrenia or bipolar disorder. Symptoms include hallucinations, confused and disturbed thoughts or lack of self-awareness. Recent studies have found that psychotic patients can be objectively screened using graph-theoretical algorithms for speech analysis. This analysis often relies in manually executed tasks such as syntagma generation, text splitting or manual feature selection for classification. To solve this fundamental limitation, we use three fully-automated text analysis tools graph generation methods. In addition, since aspects of psychosis may be manifested in semantic aspects of speech, we also developed a semantic features index based on speech coherence. We show that using this combined approach, classifications obtained from automatic techniques are higher than 85 % in a database of 20 schizophrenic patients, with similar results to previous works. In summary, here we develop and validate a new tool for automated speech processing which includes semantic and structural aspects. The tool performs similar to manual screening procedures providing a new method to complement standard psychometric scales and fostering automated psychiatric diagnosis.
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Acknowledgments
This research was supported by University of Buenos Aires, CONICET (Argentina) and ANPCyT (Argentina). MS is sponsored by James McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition. DFS is sponsored by Microsoft Faculty Fellowship.
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Carrillo, F. et al. (2016). Automated Speech Analysis for Psychosis Evaluation. In: Rish, I., Langs, G., Wehbe, L., Cecchi, G., Chang, Km., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. MLINI MLINI 2013 2014. Lecture Notes in Computer Science(), vol 9444. Springer, Cham. https://doi.org/10.1007/978-3-319-45174-9_4
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DOI: https://doi.org/10.1007/978-3-319-45174-9_4
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