The worldwide rising trend of autism spectrum disorder (ASD) calls for innovative and efficacious techniques for assessment and treatment. Virtual reality (VR) technology gains theoretical support from rehabilitation and pedagogical theories and offers a variety of capabilities in educational and interventional contexts with affordable products. VR is attracting increasing attention in the medical and healthcare industry, as it provides fully interactive three-dimensional simulations of real-world settings and social situations, which are particularly suitable for cognitive and performance training, including social and interaction skills. This review article offers a summary of current perspectives and evidence-based VR applications for children with ASD, with a primary focus on social communication, including social functioning, emotion recognition, and speech and language. Technology- and design-related limitations, as well as disputes over the application of VR to autism research and therapy, are discussed, and future directions of this emerging field are highlighted with regards to application expansion and improvement, technology enhancement, linguistic diversity, and the development of theoretical models and brain-based research.

Affective prosody recognition is an important area of research in autism spectrum conditions where difficulties in social cognition have been frequently observed. To probe into the mixed results reported in the literature, we conducted a systematic review with meta-analysis and examined potential factors that could explain the inconsistent results. Our literature search included six electronic databases for studies that compared the affective prosody recognition performance in individuals with autism spectrum condition with typically developing participants, which yielded 23 papers eligible for quantitative synthesis. Using a random-effects model, we obtained a moderate-to-large pooled effect (Hedges’ g = −0.63) for the overall affective prosody recognition performance of autism spectrum condition participants, which, however, reduced substantially (to −0.26) and became non-significant after the correction for publication bias. The number of answer codes was found to be a significant moderator for the effect estimate, whereas the number of speakers was not. Moreover, the magnitude of the pooled effect estimate varied across emotions. The findings suggested moderate differences in affective prosody recognition ability between autism spectrum condition and typically developing individuals, which reduced to marginal difficulties for autism spectrum condition when the impact of publication bias was taken into account. Diversity in the number of answer codes could have differential effects on affective prosody recognition performance in autism spectrum condition, which varied across emotions. The present review and meta-analysis demonstrated the insufficiency of research on affective prosody recognition in autism spectrum condition, highlighting a need for further exploration of the contributors and underlying mechanisms for specific affective prosody recognition difficulties. Lay Differences in understanding others’ emotions and attitudes through features in speech (e.g. intonation) have been observed in individuals with autism spectrum conditions, which contribute greatly to their social communication challenges. However, some studies reported that individuals with autism spectrum condition performed comparably to typically developing individuals on affective prosody recognition. Here, we provide a comprehensive review with statistical analysis of 23 existing studies on this topic to examine potential factors that could explain the discrepancies. Compared with typically developing individuals, autism spectrum condition participants generally appeared to encounter more difficulties in affective prosody recognition. But this finding was likely due to the tendency of the existing research to overly focus on deficits in autism. The affective prosody recognition performance in individuals with autism spectrum condition was closely related to the number of answer options offered to them. Moreover, the degree of difficulty in affective prosody recognition encountered by individuals with autism spectrum condition varied across emotions. The findings of this systematic review highlighted the need for further research on affective prosody recognition in autism (e.g. studies that include tonal language speakers and autism spectrum condition individuals with lower cognitive or verbal abilities).

Purpose: Numerous studies have identified individuals with autism spectrum disorder (ASD) with deficits in unichannel emotion perception and multisensory integration. However, only limited research is available on multichannel emotion perception in ASD. The purpose of this review was to seek conceptual clarification, identify knowledge gaps, and suggest directions for future research.Method: We conducted a scoping review of the literature published between 1989 and 2021, following the 2005 framework of Arksey and O'Malley. Data relating to study characteristics, task characteristics, participant information, and key findings on multichannel processing of emotion in ASD were extracted for the review.Results: Discrepancies were identified regarding multichannel emotion perception deficits, which are related to participant age, developmental level, and task demand. Findings are largely consistent regarding the facilitation and compensation of congruent multichannel emotional cues and the interference and disruption of incongruent signals. Unlike controls, ASD individuals demonstrate an overreliance on semantics rather than prosody to decode multichannel emotion.Conclusions: The existing literature on multichannel emotion perception in ASD is limited, dispersed, and disassociated, focusing on a variety of topics with a wide range of methodologies. Further research is necessary to quantitatively examine the impact of methodological choice on performance outcomes. An integrated framework of emotion, language, and cognition is needed to examine the mutual influences between emotion and language as well as the cross-linguistic and cross-cultural differences.Supplemental Material: https://doi.org/10.23641/asha.19386176

Virtual reality (VR) technology gains theoretical support from rehabilitation and pedagogical theories and offers a variety of capabilities in educational and interventional contexts with affordable products. VR is attracting increasing attention in the medical and healthcare industry as it provides fully interactive three-dimensional simulations of real-world settings and social situations, which are particularly suitable for cognitive and performance training including social and interaction skills. The worldwide rising trend in the prevalence of autism spectrum disorder calls for innovative and efficacious techniques for assessment and treatment. The article offers a summary of current perspectives and evidence-based applications of VR technology as an educational and intervention tool for individuals with autism spectrum disorder, with a primary focus on social communication including social functioning, emotion recognition, and speech and language. Technology- and design-related limitations as well as the disputes over the application of virtual reality to autism research and therapy are discussed and future directions of this emerging field are highlighted with regards to application expansion and improvement, technology enhancement, and the development of brain-based research and theoretical models.

Aphasic discourse has been investigated through two major approaches: a micro-linguistic approach and a macro one, but the separate analysis of the micro and macro aspects of aphasic discourse has led to a noticeable gap between them. Cohesion analysis is one of the possible ways that can directly connect these two aspects. However, few studies have investigated cohesion in aphasic discourse in an integrated manner. The present study employs a mixed-methods approach to examine whether and how patients with fluent and non-fluent stroke-induced aphasia differ from normal individuals in the cohesion of their discourse, aiming to provide a more comprehensive understanding of this issue. We compared the use of cohesive devices in the discourse of 7 non-fluent aphasics (4 males, mean age = 70.9) and 9 fluent aphasics (4 males, mean age = 70.7) against 16 non-aphasic controls (NACs) (8 males, mean age = 71.0). Transcripts were analysed and conclusions were drawn based on the combination of quantitative and qualitative observations. As predicted, discourse by aphasic participants is less cohesive than that by non-aphasic participants and the three groups’ discourse differs from each other in the distribution of cohesion categories, with non-fluent aphasics having more trouble in using grammatical cohesive devices while fluent aphasics more severely affected in constructing lexical cohesion. Results suggest that cohesion in post-stroke aphasic discourse may vary between different aphasia types and thus can be rather complicated. Additional work involving more aphasia types and more dimensions of discourse cohesion is needed to provide further insight into this question.

Purpose: As artificial intelligence (AI) takes an increasingly prominent role in health care, a growing body of research is being dedicated to its application in the investigation of communication sciences and disorders (CSD). This study aims to provide a comprehensive overview, serving as a valuable resource for researchers, developers, and professionals seeking to comprehend the evolving landscape of AI in CSD research. Method: We conducted a bibliometric analysis of AI-based research in the discipline of CSD published up to December 2023. Utilizing the Web of Science and Scopus databases, we identified 15,035 publications, with 4,375 meeting our inclusion criteria. Based on the bibliometric data, we examined publication trends and patterns, characteristics of research activities, and research hotspot tendencies. Results: From 1985 onwards, there has been a consistent annual increase in publications, averaging 16.51%, notably surging from 2012 to 2023. The primary communication disorders studied include autism, aphasia, dysarthria, Parkinson's disease, and Alzheimer's disease. Noteworthy AI models instantiated in CSD research encompass support vector machine, convolutional neural network, and hidden Markov model, among others. Conclusions: Compared to AI applications in other fields, the adoption of AI in CSD has lagged slightly behind. While CSD studies primarily use classical machine learning techniques, there is a growing trend toward the integration of deep learning methods. AI technology offers significant benefits for both research and clinical practice in CSD, but it also presents certain challenges. Moving forward, collaboration among technological, research, and clinical domains is essential to empower researchers and speech-language pathologists to effectively leverage AI technology for the study, diagnosis, assessment, and rehabilitation of CSD. Supplemental Material: https://doi.org/10.23641/asha.27162564