Using Language Processing and Speech Analysis for the Identification of Psychosis and Other Disorders

Cheryl Mary Corcoran, Guillermo A. Cecchi

Research output: Contribution to journalReview articlepeer-review

46 Scopus citations


Increasingly, data-driven methods have been implemented to understand psychopathology. Language is the main source of information in psychiatry and represents “big data” at the level of the individual. Language and behavior are amenable to computational natural language processing (NLP) analytics, which may help operationalize the mental status examination. In this review, we highlight the application of NLP to schizophrenia and its risk states as an exemplar of its use, operationalizing tangential and concrete speech as reductions in semantic coherence and syntactic complexity, respectively. Other clinical applications are reviewed, including forecasting suicide risk and detecting intoxication. Challenges and future directions are discussed, including biomarker development, harmonization, and application of NLP more broadly to behavior, including intonation/prosody, facial expression and gesture, and the integration of these in dyads and during discourse. Similar NLP analytics can also be applied beyond humans to behavioral motifs across species, important for modeling psychopathology in animal models. Finally, clinical neuroscience can inform the development of artificial intelligence.

Original languageEnglish
Pages (from-to)770-779
Number of pages10
JournalBiological Psychiatry: Cognitive Neuroscience and Neuroimaging
Issue number8
StatePublished - Aug 2020


  • Language
  • Schizophrenia
  • Semantics
  • Speech graphs
  • Suicidal
  • Syntax


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