Speech processing techniques are useful for analyzing speech and language
development in children with Autism Spectrum Disorder (ASD), who are often
varied and delayed in acquiring these skills. Early identification and
intervention are crucial, but traditional assessment methodologies such as
caregiver reports are not adequate for the requisite behavioral phenotyping.
Natural Language Sample (NLS) analysis has gained attention as a promising
complement. Researchers have developed benchmarks for spoken language
capabilities in children with ASD, obtainable through the analysis of NLS. This
paper proposes applications of speech processing technologies in support of
automated assessment of children's spoken language development by
classification between child and adult speech and between speech and nonverbal
vocalization in NLS, with respective F1 macro scores of 82.6% and 67.8%,
underscoring the potential for accurate and scalable tools for ASD research and
clinical use.

本研究旨在提出使用语音处理技术进行自动化评估儿童口语发展的应用，并通过对自然语言样本分析，对使用智能辅助诊断提供了可行性，其实验结果表明 F1 宏分数分别为 82.6% 和 67.8%。