Autism Detection in Speech – A Survey (2024)

Abstract: 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. 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. Additionally, we were unable to find research combining both features from audio and transcripts.

## introduction with an increase in people with autism spectrum disorder (asd), the need for supporting the detection has increased as well. therefore, we look into research results from psychology, biomedicine, as well as natural language processing (nlp) to gain an insight how the three fields can support each other. ## bio-/med-/psych in order to find identifiers for asd in speech, it is important to take a look at the medical descriptions and findings. ## nlp research in line with the medical findings, there are corresponding approaches in natural language processing. in even though the first entry in the table @xcite is not focusing on markers to be used in a nlp setting, it can be used as such (see section 3.1). although we summarize the participants to be 22 asd children, it is noteworthy to recognize that the authors differentiate between 17 children with autism and 5 with asd. please note that parish-morris et al. (2016) included 18 non-autistic individuals into their study who have been diagnosed with other medical issues. these individuals are 94% male and are assigned to the nt individuals in the table. the mean age for the non-autistic group with other medical diagnoses is 10.29 years. as the authors only provide percentage information on the female-to-male ratio of their participants, the specification of the amount of females in the "support" column differs from the other rows. out of the 35 nt participants, 53% are female, whereas only 25% of the 65 asd participants are female. the numbers of the support in the study of lau et al. (2022) are derived from text and supplemental material (which are conclusive and match) as the numbers do not align with the numbers in in the following sections, we take a closer look into the aforementioned experiments. ## discussion & conclusion this survey takes a look at research in the detection of identifier of autism in speech. while there is already some research, there are still some observable shortcomings. firstly, the mentioned studies show a massive under-representation of females in autism studies in general. while there are mostly at least some females participating the the mentioned studies, in comparison to their male counterparts, they make up fewer of the participants. this shows even more so in nlp approaches, which might be because of the lack of data gathered from females on the spectrum. secondly, it is noticeable that most nlp experiments use traditional machine learning approaches like svms, naive bayes or linear regession. interestingly, there are very few experiments conducted with transformers or deep learning methods in general. further research should therefore investigate, whether transformers or other deep learning methods might be a good fit for the classification of asd and possibly even improve the results we see so far. if this is not the case, it might be possible, that simpler algorithms fit the task better, which should also be addressed in further research. however, the lack of data might be another possible reason for the focus on more traditional methods as they require less training data. thirdly, while there is some research on the transcripts, compared to the amount of experiments performed on audio data, there is considerably less research on transcribed data. it should be investigated if nlp approaches on the transcripts can reach good results in detecting identifiers of autism and whether it can be further improved. lastly, we could not find any research combining the features from both the transcriptions and the audio input. future work should therefore investigate, if features from either could improve the results of the other or if they maybe even hinder each other in getting good results.