Eric J Tan1, Denny Meyer2, Erica Neill3, Susan L Rossell3. 1. Centre for Mental Health, Swinburne University of Technology, Melbourne, Australia; Department of Psychiatry, St. Vincent's Hospital, Melbourne, Australia. Electronic address: erictan@swin.edu.au. 2. Centre for Mental Health, Swinburne University of Technology, Melbourne, Australia. 3. Centre for Mental Health, Swinburne University of Technology, Melbourne, Australia; Department of Psychiatry, St. Vincent's Hospital, Melbourne, Australia.
Abstract
BACKGROUND: Speech disturbances are a recognised aspect of schizophrenia that may have potential utility as a diagnostic indicator. Recent advances in quantitative speech assessment methods have led to more reproducible and precise metrics making this possible. The current study sought firstly to characterise the speech profile of schizophrenia patients using quantitative speech measures, then examine the diagnostic utility of these measures and explore their relationship to symptoms. METHODS: Speech recordings from 43 schizophrenia/schizoaffective disorder (SZ) patients and 46 healthy controls (HC) were obtained and transcribed. Cognitive and symptom measures were also administered. RESULTS: Compared to HCs, SZ patients had higher incidences of aberrance across five types of quantitative speech variables: utterances, single words, time/speaking rate, turns and formulation errors, but not pauses. Based on two machine learning algorithms, 21 speech variables across the same five speech variable types (again not including pauses) were identified as significant classifiers for a schizophrenia diagnosis with 90-100% specificity and 80-90% sensitivity for both models. Selective relationships were also observed between these speech variables and only positive, disorganisation, excitement and formal thought disorder symptoms. CONCLUSIONS: The findings support pervasive speech impairments in schizophrenia patients relative to HCs, and the potential diagnostic utility of these speech disturbances. Continued work is needed to build the evidence base for quantitative speech assessment as a future objective diagnostic tool for schizophrenia. It holds the promise of improved diagnostic accuracy leading to increased treatment efficacy and better patient outcomes.
BACKGROUND: Speech disturbances are a recognised aspect of schizophrenia that may have potential utility as a diagnostic indicator. Recent advances in quantitative speech assessment methods have led to more reproducible and precise metrics making this possible. The current study sought firstly to characterise the speech profile of schizophrenia patients using quantitative speech measures, then examine the diagnostic utility of these measures and explore their relationship to symptoms. METHODS: Speech recordings from 43 schizophrenia/schizoaffective disorder (SZ) patients and 46 healthy controls (HC) were obtained and transcribed. Cognitive and symptom measures were also administered. RESULTS: Compared to HCs, SZ patients had higher incidences of aberrance across five types of quantitative speech variables: utterances, single words, time/speaking rate, turns and formulation errors, but not pauses. Based on two machine learning algorithms, 21 speech variables across the same five speech variable types (again not including pauses) were identified as significant classifiers for a schizophrenia diagnosis with 90-100% specificity and 80-90% sensitivity for both models. Selective relationships were also observed between these speech variables and only positive, disorganisation, excitement and formal thought disorder symptoms. CONCLUSIONS: The findings support pervasive speech impairments in schizophrenia patients relative to HCs, and the potential diagnostic utility of these speech disturbances. Continued work is needed to build the evidence base for quantitative speech assessment as a future objective diagnostic tool for schizophrenia. It holds the promise of improved diagnostic accuracy leading to increased treatment efficacy and better patient outcomes.
Authors: Maria Francisca Alonso-Sánchez; Sabrina D Ford; Michael MacKinley; Angélica Silva; Roberto Limongi; Lena Palaniyappan Journal: Schizophrenia (Heidelb) Date: 2022-04-12
Authors: Liangbing Liang; Angélica M Silva; Peter Jeon; Sabrina D Ford; Michael MacKinley; Jean Théberge; Lena Palaniyappan Journal: Front Hum Neurosci Date: 2022-08-05 Impact factor: 3.473