Several studies have demonstrated that the severity of social communication problems, a core symptom of Autism Spectrum Disorder (ASD), is correlated with specific speech characteristics of ASD individuals. This suggests that it may be possible to develop speech analysis algorithms that can quantify ASD symptom severity from speech recordings in a direct and objective manner. Here we demonstrate the utility of a new open-source AI algorithm, ASDSpeech, which can analyze speech recordings of ASD children and reliably quantify their social communication difficulties across multiple developmental timepoints. The algorithm was trained and tested on the largest ASD speech dataset available to date, which contained 99,193 vocalizations from 197 ASD children recorded in 258 Autism Diagnostic Observation Schedule, 2nd edition (ADOS-2) assessments. ASDSpeech was trained with acoustic and conversational features extracted from the speech recordings of 136 children, who participated in a single ADOS-2 assessment, and tested with independent recordings of 61 additional children who completed two ADOS-2 assessments, separated by 1-2 years. Estimated total ADOS-2 scores in the test set were significantly correlated with actual scores when examining either the first (r(59) = 0.544, P < 0.0001) or second (r(59) = 0.605, P < 0.0001) assessment. Separate estimation of social communication and restricted and repetitive behavior symptoms revealed that ASDSpeech was particularly accurate at estimating social communication symptoms (i.e., ADOS-2 social affect scores). These results demonstrate the potential utility of ASDSpeech for enhancing basic and clinical ASD research as well as clinical management. We openly share both algorithm and speech feature dataset for use and further development by the community.

中文翻译：

---

使用 ASDSpeech 可靠地量化自闭症儿童社会症状的严重程度

多项研究表明，作为自闭症谱系障碍 (ASD) 的核心症状，社交沟通问题的严重程度与 ASD 个体的特定言语特征相关。这表明有可能开发出语音分析算法，以直接、客观的方式从语音记录中量化 ASD 症状的严重程度。在这里，我们展示了一种新的开源人工智能算法 ASDSpeech 的实用性，它可以分析 ASD 儿童的语音录音，并可靠地量化他们在多个发展时间点的社交沟通困难。该算法在迄今为止最大的 ASD 语音数据集上进行了训练和测试，该数据集包含 258 个自闭症诊断观察表第二版 (ADOS-2) 评估中记录的 197 名 ASD 儿童的 99,193 个发声。ASDSpeech 使用从参加单次 ADOS-2 评估的 136 名儿童的语音录音中提取的声学和会话特征进行训练，并使用另外 61 名完成两次 ADOS-2 评估（相隔 1-2 年）的儿童的独立录音进行测试。在检查第一次（r(59) = 0.544，P < 0.0001）或第二次（r(59) = 0.605，P < 0.0001）评估时，测试集中的估计总 ADOS-2 分数与实际分数显着相关。对社交沟通以及受限和重复行为症状的单独估计表明，ASDSpeech 在估计社交沟通症状（即 ADOS-2 社交情感评分）方面特别准确。这些结果证明了 ASDpeech 在加强 ASD 基础和临床研究以及临床管理方面的潜在效用。我们公开共享算法和语音特征数据集，供社区使用和进一步开发。