2021
DOI: 10.1007/s10803-021-05123-4
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Linguistic Tone and Non-Linguistic Pitch Imitation in Children with Autism Spectrum Disorders: A Cross-Linguistic Investigation

Abstract: The conclusions on prosodic pitch features in autism spectrum disorders (ASD) have primarily been derived from studies in non-tonal language speakers. This cross-linguistic study evaluated the performance of imitating Cantonese lexical tones and their non-linguistic (nonspeech) counterparts by Cantonese-and Mandarin-speaking children with and without ASD. Acoustic analyses showed that, compared with typically developing peers, children with ASD exhibited increased pitch variations when imitating lexical tones,… Show more

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Cited by 11 publications
(11 citation statements)
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“…Perhaps a more revealing aspect of Model 1 is its cross-linguistic design, examining both English and Cantonese utterance samples. Relatively few studies [ 3 , 34 – 36 ] have examined prosody in ASD in languages prosodically and typologically distinct from English [ 41 ]. All of these studies focused only on pitch, and only one [ 36 ] applied a cross-linguistic design to allow direct comparisons of prosody across multiple languages, in the same speech sampling context.…”
Section: Discussionmentioning
confidence: 99%
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“…Perhaps a more revealing aspect of Model 1 is its cross-linguistic design, examining both English and Cantonese utterance samples. Relatively few studies [ 3 , 34 – 36 ] have examined prosody in ASD in languages prosodically and typologically distinct from English [ 41 ]. All of these studies focused only on pitch, and only one [ 36 ] applied a cross-linguistic design to allow direct comparisons of prosody across multiple languages, in the same speech sampling context.…”
Section: Discussionmentioning
confidence: 99%
“…Relatively few studies [ 3 , 34 – 36 ] have examined prosody in ASD in languages prosodically and typologically distinct from English [ 41 ]. All of these studies focused only on pitch, and only one [ 36 ] applied a cross-linguistic design to allow direct comparisons of prosody across multiple languages, in the same speech sampling context. The ML algorithm employed in Model 1 was able to classify ASD diagnosis using rhythm-relevant acoustic features (ENV, IMF, and TMS) derived from both English and Cantonese, revealing strong performance for classification (median AUCs ∼ 0.9) in both languages respectively.…”
Section: Discussionmentioning
confidence: 99%
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