Godel Michel, Robain François, Journal Fiona, Kojovic Nada, Latrèche Kenza, Dehaene-Lambertz Ghislaine, Schaer Marie
Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland.
Cognitive Neuroimaging Unit, CNRS ERL 9003, INSERM U992, CEA, Université Paris-Saclay, NeuroSpin Center, Gif/Yvette, France.
NPJ Digit Med. 2023 May 29;6(1):99. doi: 10.1038/s41746-023-00845-4.
Atypical prosody in speech production is a core feature of Autism Spectrum Disorder (ASD) that can impact everyday life communication. Because the ability to modulate prosody develops around the age of speech acquisition, it might be affected by ASD symptoms and developmental delays that emerge at the same period. Here, we investigated the existence of a prosodic signature of developmental level and ASD symptom severity in a sample of 74 autistic preschoolers. We first developed an original diarization pipeline to extract preschoolers' vocalizations from recordings of naturalistic social interactions. Using this novel approach, we then found a robust voice quality signature of ASD developmental difficulties in preschoolers. Furthermore, some prosodic measures were associated with one year later outcome in participants who had not acquired speech yet. Altogether, our results highlight the potential benefits of automatized diarization algorithms and prosodic metrics for digital phenotyping in psychiatry, helping clinicians establish early diagnosis and prognosis.
言语产生中的非典型韵律是自闭症谱系障碍(ASD)的一个核心特征,它会影响日常生活中的交流。由于调节韵律的能力在语言习得年龄左右发展,它可能会受到同期出现的ASD症状和发育迟缓的影响。在这里,我们调查了74名自闭症学龄前儿童样本中发育水平和ASD症状严重程度的韵律特征。我们首先开发了一个原始的语音分离管道,从自然主义社交互动的录音中提取学龄前儿童的发声。使用这种新方法,我们随后在学龄前儿童中发现了ASD发育困难的一种强大的语音质量特征。此外,一些韵律测量与尚未习得语言的参与者一年后的结果相关。总之,我们的结果突出了自动化语音分离算法和韵律指标在精神病学数字表型分析中的潜在益处,有助于临床医生进行早期诊断和预后评估。