  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.
