Atypical prosodic realization by Mandarin-speaking autistic children: Evidence from tone sandhi and neutral tone

Guo, Chengyu; Chen, Fei; Yan, Jinting; Gao, Xiaotian; Zhu, Meifang

doi:10.1016/j.jcomdis.2022.106280

Cited by 2 publications

(6 citation statements)

References 39 publications

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“…Although the pitch range and pitch standard deviation indicate changes in pitch, their combination convincingly indicates the magnitude of pitch variability [28]. In the…”

Section: Pitch Variabilitymentioning

confidence: 92%

“…Prosodic disorders of autistic individuals have prompted a fair amount of research attempts [16], but the general pattern of prosody cannot be characterized for ASD patients, considering the inconsistent findings among studies due to the large individual heterogeneity and measurement variability [12]. For example, despite the conventional description of "mono-toned" or "machine-like", autistic speech has been reported to show a higher pitch variability with accumulating evidence [3,15,22,28]. The inconsistency is further complicated by a variety of factors moderating the pitch variability, including IQ, age, gender, autistic severity, and language capacity [3,16,29].…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is noteworthy that prosodic features in natural speech have been demonstrated to be more representative and sensitive [13]. For example, accumulating studies have showed elevated pitch mean in natural speech for ASD patients, irrespective of tonal or non-tonal language speakers and across different age groups [28]. Furthermore, the pitch standard deviation of ASD groups is 82.6% larger than that of TD groups in a natural context [34].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Furthermore, the pitch standard deviation of ASD groups is 82.6% larger than that of TD groups in a natural context [34]. With high sensitivity, prosodic characteristics of natural speech are incorporated in the clinical diagnosis of ASD [16,28]. For example, prosodic features or voice quality in natural speech were used in the Autism Diagnostic Interview-Revised and Autism Diagnostic Observation Schedule-in the Second Edition (the golden standards for the assessment of ASD) and Social Responsiveness Scale to detect autistic individuals [28,35].…”

Section: Literature Reviewmentioning

confidence: 99%

“…With high sensitivity, prosodic characteristics of natural speech are incorporated in the clinical diagnosis of ASD [16,28]. For example, prosodic features or voice quality in natural speech were used in the Autism Diagnostic Interview-Revised and Autism Diagnostic Observation Schedule-in the Second Edition (the golden standards for the assessment of ASD) and Social Responsiveness Scale to detect autistic individuals [28,35]. In addition, aberrations of pitch variability in daily conversation are applied by clinicians to distinguish autistic speakers [3].…”

Section: Literature Reviewmentioning

confidence: 99%

See 4 more Smart Citations

Can Natural Speech Prosody Distinguish Autism Spectrum Disorders? A Meta-Analysis

Ma,

Xu,

Zhang

et al. 2024

Behavioral Sciences

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Natural speech plays a pivotal role in communication and interactions between human beings. The prosody of natural speech, due to its high ecological validity and sensitivity, has been acoustically analyzed and more recently utilized in machine learning to identify individuals with autism spectrum disorders (ASDs). In this meta-analysis, we evaluated the findings of empirical studies on acoustic analysis and machine learning techniques to provide statistically supporting evidence for adopting natural speech prosody for ASD detection. Using a random-effects model, the results observed moderate-to-large pooled effect sizes for pitch-related parameters in distinguishing individuals with ASD from their typically developing (TD) counterparts. Specifically, the standardized mean difference (SMD) values for pitch mean, pitch range, pitch standard deviation, and pitch variability were 0.3528, 0.6744, 0.5735, and 0.5137, respectively. However, the differences between the two groups in temporal features could be unreliable, as the SMD values for duration and speech rate were only 0.0738 and −0.0547. Moderator analysis indicated task types were unlikely to influence the final results, whereas age groups showed a moderating role in pooling pitch range differences. Furthermore, promising accuracy rates on ASD identification were shown in our analysis of multivariate machine learning studies, indicating averaged sensitivity and specificity of 75.51% and 80.31%, respectively. In conclusion, these findings shed light on the efficacy of natural prosody in identifying ASD and offer insights for future investigations in this line of research.

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“…Although the pitch range and pitch standard deviation indicate changes in pitch, their combination convincingly indicates the magnitude of pitch variability [28]. In the…”

Section: Pitch Variabilitymentioning

confidence: 92%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

Can Natural Speech Prosody Distinguish Autism Spectrum Disorders? A Meta-Analysis

Ma,

Xu,

Zhang

et al. 2024

Behavioral Sciences

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Investigating Perception to Production Transfer in Children With Cochlear Implants: A High Variability Phonetic Training Study

Zhang,

Dai,

et al. 2024

J Speech Lang Hear Res

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Purpose: This study builds upon an established effective training method to investigate the advantages of high variability phonetic identification training for enhancing lexical tone perception and production in Mandarin-speaking pediatric cochlear implant (CI) recipients, who typically face ongoing challenges in these areas. Method: Thirty-two Mandarin-speaking children with CIs were quasirandomly assigned into the training group (TG) and the control group (CG). The 16 TG participants received five sessions of high variability phonetic training (HVPT) within a period of 3 weeks. The CG participants did not receive the training. Perception and production of Mandarin tones were administered before (pretest) and immediately after (posttest) the completion of HVPT via lexical tone recognition task and picture naming task. Both groups participated in the identical pretest and posttest with the same time frame between the two test sessions. Results: TG showed significant improvement from pretest to posttest in identifying Mandarin tones for both trained and untrained speech stimuli. Moreover, perceptual learning of HVPT significantly facilitated trainees' production of T1 and T2 as rated by a cohort of 10 Mandarin-speaking adults with normal hearing, which was corroborated by acoustic analyses revealing improved fundamental frequency ( F 0) median for T1 and T2 production and enlarged F 0 movement for T2 production. In contrast, TG children's production of T3 and T4 showed nonsignificant changes across two test sessions. Meanwhile, CG did not exhibit significant changes in either perception or production. Conclusions: The results suggest a limited and inconsistent transfer of perceptual learning to lexical tone production in children with CIs, which challenges the notion of a robust transfer and highlights the complexity of the interaction between perceptual training and production outcomes. Further research on individual differences with a longitudinal design is needed to optimize the training protocol or tailor interventions to better meet the diverse needs of learners.

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Atypical prosodic realization by Mandarin-speaking autistic children: Evidence from tone sandhi and neutral tone

Cited by 2 publications

References 39 publications

Can Natural Speech Prosody Distinguish Autism Spectrum Disorders? A Meta-Analysis

Can Natural Speech Prosody Distinguish Autism Spectrum Disorders? A Meta-Analysis

Investigating Perception to Production Transfer in Children With Cochlear Implants: A High Variability Phonetic Training Study

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