There has been a range of studies of how autism is displayed in voice, speech, and language. We analyse studies from the biomedical, as well as the psychological domain, but also from the NLP domain in order to find linguistic, prosodic and acoustic cues that could indicate autism. Our survey looks at all three domains. We define autism and which comorbidities might influence the correct detection of the disorder. We especially look at observations such as verbal and semantic fluency, prosodic features, but also disfluencies and speaking rate. We also show word-based approaches and describe machine learning and transformer-based approaches both on the audio data as well as the transcripts. Lastly, we conclude, while there already is a lot of research, female patients seem to be severely under-researched. Also, most NLP research focuses on traditional machine learning methods instead of transformers which could be beneficial in this context. Additionally, we were unable to find research combining both features from audio and transcripts.

翻译：围绕自闭症在声音、言语及语言中的表现已有一系列研究。为发掘可指示自闭症的语言、韵律及声学线索，我们分析了来自生物医学、心理学以及自然语言处理领域的研究。本综述涵盖上述三个领域，首先定义自闭症概念及其可能干扰正确诊断的共病因素，尤其关注言语与语义流畅性、韵律特征，以及言语不流畅度和语速等观察指标。同时展示基于词汇的分析方法，并阐述针对音频数据及转录文本的机器学习与基于Transformer的方法。最后总结指出：尽管已有大量研究，但女性患者群体仍未得到充分关注；多数自然语言处理研究仍集中于传统机器学习方法，而可能更有潜力的Transformer模型应用不足；此外，尚未发现将音频与转录文本特征相结合的研究。