Questioning the status of aberrant speech patterns as psychiatric symptoms

Automated linguistic analysis in speech samples of Turkish-speaking patients with schizophrenia-spectrum disorders

Modern natural language processing (NLP) methods provide ways to objectively quantify language disturbances for potential use in diagnostic classification. We performed computerized language analysis in speech samples of 82 Turkish-speaking subjects, including 44 patients with schizophrenia spectrum disorders (SSD) and 38 healthy controls (HC). Exploratory analysis of speech samples involved 16 sentence-level semantic similarity features using SBERT (Sentence Bidirectional Encoder Representation from Text) as well as 8 generic and 8 part-of-speech (POS) features. The random forest classifier using SBERT-derived semantic similarity features achieved a mean accuracy of 85.6 % for the classification of SSD and HC. When semantic similarity features were combined with generic and POS features, the classifier's mean accuracy reached to 86.8 %. Our analysis reflected increased sentence-level semantic similarity scores in SSD. Generic and POS analyses revealed an increase in the use of verbs, proper nouns and pronouns in SSD while our results showed a decrease in the utilization of conjunctions, determiners, and both average and maximum sentence length in SSD compared to HC. Quantitative language features were correlated with the expressive deficit domain of BNSS (Brief Negative Symptom Scale) as well as with the duration of illness. These findings from Turkish-speaking interviews contribute to the growing evidence-based NLP-derived assessments in non-English-speaking patients.

Language and Psychosis: Tightening the Association

This special issue of DISCOURSE in Psychosis focuses on the role of language in psychosis, including the relationships between formal thought disorder and conceptual disorganization, with speech and language markers and the neural mechanisms underlying these features in psychosis. It also covers the application of computational techniques in the study of language in psychosis, as well as the potential for using speech and language data for digital phenotyping in psychiatry.

Depressive symptoms are specifically related to speech pauses in schizophrenia spectrum disorders

Depression is a common and debilitating mental illness associated with sadness and negativity and is often comorbid with other psychiatric conditions, such as schizophrenia. Depressive symptoms are presently primarily assessed through clinical interviews, however there are other behavioural indicators being investigated as more objective methods of depressive symptom assessment. The present study aimed to evaluate the utility of assessing depression using quantitative speech parameters by comparing speech between 23 schizophrenia/schizoaffective patients with clinically significant depressive symptoms (DP) 19 schizophrenia/schizoaffective patients without depressive symptoms (NDP) and 22 healthy controls with no psychiatric history (HC). Participant audio recordings were transcribed and analyzed to extract five types of speech variables: utterances, words, speaking rate, formulation errors and pauses. The results indicated that DP patients produced significantly more pauses within utterances, and had more utterances with pauses compared to NDP patients and HCs (p = <.05), who performed similarly to each other. Word, speaking rate and formulation errors variables were not significantly different between the patient groups (p > .05). The findings suggest that depressive symptoms may have a specific relationship to speech pauses, and support the potential future use of speech pause assessments as an alternative and objective depression rating and monitoring tool.

Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool

BACKGROUND

Clinicians routinely use impressions of speech as an element of mental status examination. In schizophrenia-spectrum disorders, descriptions of speech are used to assess the severity of psychotic symptoms. In the current study, we assessed the diagnostic value of acoustic speech parameters in schizophrenia-spectrum disorders, as well as its value in recognizing positive and negative symptoms.

METHODS

Speech was obtained from 142 patients with a schizophrenia-spectrum disorder and 142 matched controls during a semi-structured interview on neutral topics. Patients were categorized as having predominantly positive or negative symptoms using the Positive and Negative Syndrome Scale (PANSS). Acoustic parameters were extracted with OpenSMILE, employing the extended Geneva Acoustic Minimalistic Parameter Set, which includes standardized analyses of pitch (F0), speech quality and pauses. Speech parameters were fed into a random forest algorithm with leave-ten-out cross-validation to assess their value for a schizophrenia-spectrum diagnosis, and PANSS subtype recognition.

RESULTS

The machine-learning speech classifier attained an accuracy of 86.2% in classifying patients with a schizophrenia-spectrum disorder and controls on speech parameters alone. Patients with predominantly positive v. negative symptoms could be classified with an accuracy of 74.2%.

CONCLUSIONS

Our results show that automatically extracted speech parameters can be used to accurately classify patients with a schizophrenia-spectrum disorder and healthy controls, as well as differentiate between patients with predominantly positive v. negatives symptoms. Thus, the field of speech technology has provided a standardized, powerful tool that has high potential for clinical applications in diagnosis and differentiation, given its ease of comparison and replication across samples.

Investigating the diagnostic utility of speech patterns in schizophrenia and their symptom associations

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.

Implementation of automatic speech analysis for early detection of psychiatric symptoms: What do patients want?

Psychiatry is in dire need of a method to aid early detection of symptoms. Recent developments in automatic speech analysis prove promising in this regard, and open avenues for implementation of speech-based applications to detect psychiatric symptoms. The current survey was conducted to assess positions with regard to speech recordings among a group (n = 675) of individuals who experience psychiatric symptoms. Overall, respondents are open to the idea of speech recordings in light of their mental welfare. Importantly, concerns with regard to privacy were raised. Given that speech recordings are privacy sensitive, this requires special attention upon implementation of automatic speech analysis techniques. Furthermore, respondents indicated a preference for speech recordings in the presence of a clinician, as opposed to a recording made at home without the clinician present. In developing a speech marker for psychiatry, close collaboration with the intended users is essential to arrive at a truly valid and implementable method.