Psychological Medicine cambridge.org/psm Correspondence Cite this article: DeSouza DD, Tang SX, Danilewitz M (2022). The burgeoning role of speech and language assessment in schizophrenia spectrum disorders. Psychological Medicine 1–2. https://doi.org/ 10.1017/S0033291722001325 Received: 22 March 2022 Revised: 27 March 2022 Accepted: 19 April 2022 Author for correspondence: Danielle D. DeSouza, E-mail: desouzad@stanford.edu © The Author(s), 2022. Published by Cambridge University Press The burgeoning role of speech and language assessment in schizophrenia spectrum disorders Danielle D. DeSouza 1,2 , Sunny X. Tang 3 and Marlon Danilewitz 4,5 1 Winterlight Labs, Toronto, Ontario, Canada; 2 Department of Neurology and Neurological Sciences, Stanford University, Stanford, California USA; 3 Institute for Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, Glen Oaks, New York USA; 4 Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada and 5 Ontario Shores Centre for Mental Health Sciences, Whitby, Ontario, Canada Dear Editor, Advances in automated speech assessment and natural language processing (NLP) have dras- tically improved our ability to objectively detect and characterize alterations in speech and lan- guage. These technologies have growing applications for individuals with psychiatric disorders, particularly schizophrenia spectrum disorders (SSD). NLP methods have proven to be a sensi- tive, objective, and accurate means to quantify language organization and impoverishment in SSD. While the results in the literature have been mixed, some promising research has shown that acoustic speech parameters may be linked to negative symptoms of SSD and can differen- tiate individuals with SSD from controls with high accuracy (Bernardini et al., 2016). We read with enthusiasm the recently published study by de Boer and colleagues (de Boer et al., 2021) using acoustic speech parameters to classify individuals with SSD and healthy con- trols. The authors set out to overcome the limitations of previous studies by including a large sample (n = 284) and standardized acoustic speech measures, developed to promote easy rep- lication and comparability of acoustic parameters across studies. To address symptom hetero- geneity in SSD, machine learning speech classifiers evaluated how accurately patients with predominantly positive v. predominantly negative symptoms could be classified. The results revealed that individuals with SSD and controls could be classified with 86.2% accuracy using acoustic speech features alone. Furthermore, SSD patient subtypes could be distin- guished with 74.2% accuracy. The high accuracy achieved using this approach adds to the mounting evidence that speech can provide an objective way to not only accurately detect indi- viduals with SSD but also to differentiate among SSD subtypes. Despite previous successful applications of speech and language assessment in predicting SSD diagnosis (Elvevåg, Foltz, Weinberger, & Goldberg, 2007; Tang et al., 2021), few studies have differ- entiated among SSD subtypes. The current study by de Boer et al., is arguably the first to do this by using speech to characterize participants with predominantly positive or negative symptoms. Delineating between these subtypes has important clinical implications given the greater resistance of negative symptoms to treatment and strong associations with poor functional outcomes (Correll & Schooler, 2020). The authors also explored stratifying participants based on high and low dopamine receptor (DR2) occupancy drug profiles. An interesting follow-up analysis in the context of medication effects may be to separately assess individuals on clozapine, given its unique profile as the preferred treatment for individuals with treatment-resistant schizophrenia (TRS). The identification of patients with TRS has high clinical relevance. TRS is typically defined as a failure to respond to multiple antipsychotic drugs from at least two different classes pre- scribed in adequate dosages and durations. This group is estimated to account for 20–30% of all patients with schizophrenia and is one of the most disabling and costly psychiatric condi- tions, conservatively estimated to account for $34 billion in annual direct medical costs (Kennedy, Altar, Taylor, Degtiar, & Hornberger, 2014). Tools that aid in the classification of SSD subtypes, including TRS, warrant further research to facilitate early identification and objective monitoring of symptoms. In particular, under- lying biological differences may differ between patients with TRS compared to other SSD sub- types (Potkin et al., 2020) that can be informed by speech. For example, brain alterations spanning white matter tracts involved in ventral language processing (Smits, Jiskoot, & Papma, 2014) have recently been reported in TRS compared to healthy controls and other treatment-response subtypes (McNabb et al., 2020). Based on these observations, individuals with TRS may have unique speech and language symptoms which can be detected early in the course of illness, and which may inform treatment strategies. Future studies may also examine how the inclusion of both acoustic and linguistic speech parameters impacts classification model performance. As noted by the authors, previous stud- ies have evaluated linguistic content, including measures to quantify coherence, syntactic com- plexity, and language connectedness. However, acoustic and textual linguistic measures may https://doi.org/10.1017/S0033291722001325 Published online by Cambridge University Press