Semantic Characteristics of Schizophrenic Speech
This work addresses the problem of schizophrenia diagnosis for clinicians and patients, but it is incremental as it applies existing NLP tools to a new language and specific speech characteristics.
The study tackled the problem of automatically detecting schizophrenia by analyzing speech patterns, showing that controls maintain more cohesive speech and use more common adjectives and adverbs than inpatients, with experimental results indicating potential for detection.
Natural language processing tools are used to automatically detect disturbances in transcribed speech of schizophrenia inpatients who speak Hebrew. We measure topic mutation over time and show that controls maintain more cohesive speech than inpatients. We also examine differences in how inpatients and controls use adjectives and adverbs to describe content words and show that the ones used by controls are more common than the those of inpatients. We provide experimental results and show their potential for automatically detecting schizophrenia in patients by means only of their speech patterns.