Abstract
Diagnosing autism spectrum disorder (ASD) by identifying abnormal speech patterns from examiner-patient dialogues presents significant challenges due to the subtle and diverse manifestations of speech-related symptoms in affected individuals. This study presents a comprehensive approach to identify distinctive speech patterns through the analysis of examiner-patient dialogues. Utilizing a dataset of recorded dialogues, we extracted 40 speech-related features, categorized into frequency, zero-crossing rate, energy, spectral characteristics, Mel Frequency Cepstral Coefficients (MFCCs), and balance. These features encompass various aspects of speech such as intonation, volume, rhythm, and speech rate, reflecting the complex nature of communicative behaviors in ASD. We employed machine learning for both classification and regression tasks to analyze these speech features. The classification model aimed to differentiate between ASD and non-ASD cases, achieving an accuracy of 87.75%. Regression models were developed to predict speech pattern related variables and a composite score from all variables, facilitating a deeper understanding of the speech dynamics associated with ASD. The effectiveness of machine learning in interpreting intricate speech patterns and the high classification accuracy underscore the potential of computational methods in supporting the diagnostic processes for ASD. This approach not only aids in early detection but also contributes to personalized treatment planning by providing insights into the speech and communication profiles of individuals with ASD.
Abstract (translated)
通过对考官和患者对话的异常语音模式的鉴定来诊断自闭症谱系障碍（ASD）是一个具有挑战性的任务，因为受影响个体的语音相关症状 subtle 和 diverse 表现。这项研究通过分析考官和患者对话的录音数据，全面探讨了识别独特语音模式的方法。利用一个记录的对话数据集，我们提取了40个语音相关特征，分为频度、零交叉率、能量、特征、梅尔频谱系数（MFCC）和平衡。这些特征涵盖了 ASD 中各种语音方面，如语调、音量、节奏和语速，反映了 ASD 交际行为的复杂性。我们使用机器学习对分类和回归任务进行分析。分类模型旨在区分 ASD 和非 ASD 病例，达到 87.75% 的准确率。回归模型用于预测与语音模式相关的变量和复合评分，促进对 ASD 相关的语音动态的深入理解。机器学习在解释复杂语音模式和实现高度分类准确方面具有潜力，为支持 ASD 的诊断过程提供了计算方法。这种方法不仅有助于早期诊断，还有助于为 ASD 患者提供个性化的治疗计划，通过提供对 ASD 个体的语言和交流特点的洞察力。
URL
https://arxiv.org/abs/2405.05126
