In this systematic review, we analyzed and evaluated the findings of studies on prosodic features of vocal productions of people with autism spectrum disorder (ASD) in order to recognize the statistically significant, most confirmed and reliable prosodic differences distinguishing people with ASD from typically developing individuals. Using suitable keywords, three major databases including Web of Science, PubMed and Scopus, were searched. The results for prosodic features such as mean pitch, pitch range and variability, speech rate, intensity and voice duration were extracted from eligible studies. The pooled standard mean difference between ASD and control groups was extracted or calculated. Using I²statistic and Cochrane Q-test, between-study heterogeneity was evaluated. Furthermore, publication bias was assessed using funnel plot and its significance was evaluated using Egger’s and Begg’s tests. Thirty-nine eligible studies were retrieved (including 910 and 850 participants for ASD and control groups, respectively). This systematic review and meta-analysis showed that ASD group members had a significantly larger mean pitch (SMD = − 0.4, 95% CI [− 0.70, − 0.10]), larger pitch range (SMD = − 0.78, 95% CI [− 1.34, − 0.21]), longer voice duration (SMD = − 0.43, 95% CI [− 0.72, − 0.15]), and larger pitch variability (SMD = − 0.46, 95% CI [− 0.84, − 0.08]), compared with typically developing control group. However, no significant differences in pitch standard deviation, voice intensity and speech rate were found between groups. Chronological age of participants and voice elicitation tasks were two sources of between-study heterogeneity. Furthermore, no publication bias was observed during analyses (p > 0.05). Mean pitch, pitch range, pitch variability and voice duration were recognized as the prosodic features reliably distinguishing people with ASD from TD individuals.

Background: People with autism spectrum disorder (ASD) have difficulties recognizing emotions. Studies showed that virtual reality (VR) and computerized training programs might be used as potential tools for enhancing emotion recognition in such people. However, some inconsistencies were observed between the studies. Objective: In the current systematic review and meta-analysis, the potential of computerized and VR training programs were evaluated for enhancing emotion recognition in people with ASD. Method: Using PRISMA guidelines and a PICO model, eligible studies were retrieved and the pooled effect size was calculated. Results: This meta-analysis obtained the pooled effect of Cohen's d = 0.69 (95% CI: [0.49, 0.89]) that showed the positive effect of VR and computerized training on emotion recognition in people with ASD. The effectiveness was confirmed for different types of study design, and for both children and adults, while it was larger for non-VR computerized programs compared with VR counterparts. Conclusion–Due to the small sample size of this study and the substantial heterogeneity between studies, the outcomes should be considered with caution in practice. However, these outcomes can be considered for optimizing suitable computerized applications or as the hypothesis for future studies.