Toward a cumulative science of vocal markers of autism: A cross‐linguistic meta‐analysis‐based investigation of acoustic markers in American and Danish autistic children

Abstract

Acoustic atypicalities in speech production are argued to be potential markers of clinical features in autism spectrum disorder (ASD). A recent meta-analysis highlighted shortcomings in the field, in particular small sample sizes and study heterogeneity. We showcase a cumulative (i.e., explicitly building on previous studies both conceptually and statistically) yet self-correcting (i.e., critically assessing the impact of cumulative statistical techniques) approach to prosody in ASD to overcome these issues. We relied on the recommendations contained in the meta-analysis to build and analyze a cross-linguistic corpus of multiple speech productions in 77 autistic and 72 neurotypical children and adolescents (>1000 recordings in Danish and US English). We used meta-analytically informed and skeptical priors, with informed priors leading to more generalizable inference. We replicated findings of a minimal cross-linguistically reliable distinctive acoustic profile for ASD (higher pitch and longer pauses) with moderate effect sizes. We identified novel reliable differences between the two groups for normalized amplitude quotient, maxima dispersion quotient, and creakiness. However, the differences were small, and there is likely no one acoustic profile characterizing all autistic individuals. We identified reliable relations of acoustic features with individual differences (age, gender), and clinical features (speech rate and ADOS subscores). Besides cumulatively building our understanding of acoustic atypicalities in ASD, the study shows how to use systematic reviews and meta-analyses to guide the design and analysis of follow-up studies. We indicate future directions: larger and more diverse cross-linguistic datasets, focus on heterogeneity, selfcritical cumulative approaches, and open science. Lay SummaryAutistic individuals are reported to speak in distinctive ways. Distinctive vocal production can affect social interactions and social development and could represent a noninvasive way to support the assessment of autism spectrum disorder (ASD). We systematically checked whether acoustic atypicalities highlighted in previous articles could be actually found across multiple recordings and two languages. We find a minimal acoustic profile of ASD: higher pitch, longer pauses, increased hoarseness and creakiness of the voice. However, there is much individual variability (by age, sex, language, and clinical characteristics). This suggests that the search for one common "autistic voice" might be naive and more finegrained approaches are needed.

Vocal markers of autism: assessing the generalizability of machine learning models

“…The dataset used in this study consists of voice recordings collected in previous studies for other purposes and their content has been analyzed in other published research (Cantio et al, 2016; Fusaroli et al, 2022; Grossman et al, 2013). For demographic, clinical and cognitive information about the participants, see Table 1 1 .…”

“…The US English dataset included 50 autistic (187 recordings, 149 minutes) and 31 NT (122 recordings, 56 minutes) participants, retelling stories (Grossman et al, 2013). The recordings had been collected and analyzed for different purposes in previous studies (Cantio et al, 2016; Fusaroli et al, 2022; Grossman et al, 2013).…”

Different in different ways: A network-analysis approach to voice and prosody in Autism Spectrum Disorder

“…The most useful outcome for training new clinicians, for example, would be to identify a set of easily-identified acoustic features that have been shown to be characteristic of the speech of autistic people. Neither a long list of relatively complex acoustic features nor an inscrutable composite component from PCA achieves this goal (see supporting information S4 of Fusaroli et al (2021) for a discussion of the interpretation of PCA components in acoustic analysis). While likely incomplete, a list of approximately three to five intuitively understandable features may be of the greatest utility for intervention.…”

“…A recent systematic review and meta-analysis (Fusaroli, Lambrechts, Bang, Bowler, & Gaigg, 2017) highlights several potential acoustic contributors to group differences. The meta-analytic findings point to higher pitch, and increased pitch variability, number of pauses and pause duration, although replication attempts show these patterns might be not be generalizable across languages and samples (see, Fusaroli et al 2021). While some studies have found that acoustic differences map onto expert clinician ratings of autism-specific symptoms (McCann et al, 2007; Study 1, Diehl et al 2009), these findings have not always replicated, even within the same lab (Study 2, ibid;Fusaroli et al 2021).…”

Speech disturbances in schizophrenia: assessing cross-linguistic generalizability of NLP automated measures of coherence

“…To move beyond this situation, this study showcases a cumulative scientific approach capable of systematically assessing the impact of previous findings on current data and integrating the new findings into a global framework. Such a framework promotes the systematic assessment of previous findings and different automated measures of coherence across contexts and samples with different clinical, demographic, cultural and linguistic profiles (Corcoran et al, 2018; Fusaroli et al, 2021; Rybner et al, 2021). This will provide a more robust predictive performance assessment, but it may also provide more reliable foundations for theory development (e.g.…”