Computerized natural language processing (NLP) allows for objective and sensitive detection of speech disturbance, a hallmark of schizophrenia spectrum disorders (SSD). We explored several methods for characterizing speech changes in SSD (n = 20) compared to healthy control (HC) participants (n = 11) and approached linguistic phenotyping on three levels: individual words, parts-of-speech (POS), and sentence-level coherence. NLP features were compared with a clinical gold standard, the Scale for the Assessment of Thought, Language and Communication (TLC). We utilized Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art embedding algorithm incorporating bidirectional context. Through the POS approach, we found that SSD used more pronouns but fewer adverbs, adjectives, and determiners (e.g., “the,” “a,”). Analysis of individual word usage was notable for more frequent use of first-person singular pronouns among individuals with SSD and first-person plural pronouns among HC. There was a striking increase in incomplete words among SSD. Sentence-level analysis using BERT reflected increased tangentiality among SSD with greater sentence embedding distances. The SSD sample had low speech disturbance on average and there was no difference in group means for TLC scores. However, NLP measures of language disturbance appear to be sensitive to these subclinical differences and showed greater ability to discriminate between HC and SSD than a model based on clinical ratings alone. These intriguing exploratory results from a small sample prompt further inquiry into NLP methods for characterizing language disturbance in SSD and suggest that NLP measures may yield clinically relevant and informative biomarkers.

Background and Objectives We compared digital speech and language features of patients with amnestic Alzheimer disease (aAD) or logopenic variant primary progressive aphasia (lvPPA) in a biologically confirmed cohort and related these features to neuropsychiatric test scores and CSF analytes. Methods We included patients with aAD or lvPPA with CSF (phosphorylated tau ([p-tau]/β-amyloid [Aβ] ≥0.09, and total tau/Aβ ≥0.34) or autopsy confirmation of AD pathology and age-matched healthy controls (HC) recruited at the Frontotemporal Degeneration Center of the University of Pennsylvania for a cross-sectional study. We extracted speech and language variables with automated lexical and acoustic pipelines from participants9 oral picture descriptions. We compared the groups and correlated distinct features with clinical ratings and CSF p-tau levels. Results We examined patients with aAD (n = 44; age 62 ± 8 years; 24 women; Mini-Mental State Examination [MMSE] score 21.1 ± 4.8) or lvPPA (n = 21; age 64.1 ± 8.2 years; 11 women; MMSE score 23.0 ± 4.2) and HC (n = 28; age 65.9 ± 5.9 years, 15 women; MMSE score 29 ± 1). Patients with lvPPA produced fewer verbs (10.5 ± 2.3; p = 0.001) and adjectives (2.7 ± 1.3, p = 0.019) and more fillers (7.4 ± 3.9; p = 0.022) with lower lexical diversity (0.84 ± 0.1; p = 0.05) and higher pause rate (54.2 ± 19.2; p = 0.015) than individuals with aAD (verbs 12.5 ± 2; adjectives 3.8 ± 2; fillers 4.9 ± 4.5; lexical diversity 0.87 ± 0.1; pause rate 45.3 ± 12.8). Both groups showed some shared language impairments compared with HC. Word frequency (MMSE score: β = −1.6, p = 0.009; Boston Naming Test [BNT] score: β = −4.36, p < 0.001), adverbs (MMSE score: β = −1.9, p = 0.003; BNT score: β = −2.41, p = 0.041), pause rate (MMSE score: β = −1.21, p = 0.041; BNT score: β = −2.09, p = 0.041), and word length (MMSE score: β = 1.75, p = 0.001; BNT score: β = 2.94, p = 0.003) were significantly correlated with both MMSE and BNT scores, but other measures were not correlated with MMSE and/or BNT score. Prepositions (r = −0.36, p = 0.019), nouns (r = −0.31, p = 0.047), speech segment duration (r = −0.33, p = 0.032), word frequency (r = 0.33, p = 0.036), and pause rate (r = 0.34, p = 0.026) were correlated with patients9 CSF p-tau levels. Discussion Our measures captured language and speech differences between the 2 phenotypes that traditional language-based clinical assessments failed to identify. This work demonstrates the potential of natural speech in reflecting underlying variants with AD pathology.

Background: Autistic girls are underdiagnosed compared to autistic boys, even when they experience similar clinical impact. Research suggests that girls present with distinct symptom profiles across a variety of domains, such as language, which may contribute to their underdiagnosis. In this study, we examine sex differences in the temporal dynamics of natural conversations between naïve adult confederates and school-aged children with or without autism, with the goal of improving our understanding of conversational behavior in autistic girls and ultimately improving identification. Methods: Forty-five school-aged children with autism (29 boys and 16 girls) and 47 non-autistic/neurotypical (NT) children (23 boys and 24 girls) engaged in a 5-min “get-to-know-you” conversation with a young adult confederate that was unaware of children’s diagnostic status. Groups were matched on IQ estimates. Recordings were time-aligned and orthographically transcribed by trained annotators. Several speech and pause measures were calculated. Groups were compared using analysis of covariance models, controlling for age. Results: Autistic girls used significantly more words than autistic boys, and produced longer speech segments than all other groups. Autistic boys spoke more slowly than NT children, whereas autistic girls did not differ from NT children in total word counts or speaking rate. Autistic boys interrupted confederates’ speech less often and produced longer between-turn pauses (i.e., responded more slowly when it was their turn) compared to other children. Within-turn pause duration did not differ by group. Limitations: Our sample included verbally fluent children and adolescents aged 6–15 years, so our study results may not replicate in samples of younger children, adults, and individuals who are not verbally fluent. The results of this relatively small study, while compelling, should be interpreted with caution and replicated in a larger sample. Conclusion: This study investigated the temporal dynamics of everyday conversations and demonstrated that autistic girls and boys have distinct natural language profiles. Specifying differences in verbal communication lays the groundwork for the development of sensitive screening and diagnostic tools to more accurately identify autistic girls, and could inform future personalized interventions that improve short- and long-term social communication outcomes for all autistic children.