Automatic Speech Recognition and Assessment Systems Incorporated into Digital Therapeutics for Children with Autism Spectrum Disorder

1. American Psychiatric Association: Diagnostic and statistical manual of mental disorders: DSM-5, 5th edn. American Psychiatric Publishing, Washington, D.C. (2013)

   Google Scholar 

2. Justice, L.M.: Communication Sciences and Disorder: An Introduction, 1st edn. Merrill/Prentice Hall, Upper Saddle River, NJ (2006)

   Google Scholar 

3. Washington, P., et al.: Data-driven diagnostics and the potential of mobile artificial intelligence for digital therapeutic phenotyping in computational psychiatry. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 5(8), 759–769 (2020)

   Google Scholar 

4. Cognoa Homepage. https://cognoa.com/. Accessed 25 Mar 2024

5. Bowden, M., et al.: A systematic review and narrative analysis of digital speech biomarkers in motor neuron disease. NPJ Digit. Med. 6(1), 228 (2023)

   Article  Google Scholar 

6. Attwell, G.A., Bennin, K.E., Tekinerdogan, B.: A systematic review of online speech therapy systems for intervention in childhood speech communication disorders. Sensors 22(24), 9713 (2022)

   Google Scholar 

7. Choi, M.J., Kim, H., Nah, H.W., Kang, D.W.: Digital therapeutics: emerging new therapy for neurologic deficits after stroke. J. Stroke 21(3), 242–258 (2019)

   Article  Google Scholar 

8. Turner, L.M., Stone, W.L., Pozdol, S.L., Coonrod, E.E.: Follow-up of children with autism spectrum disorders from age 2 to age 9. Autism 10(3), 243–265 (2006)

   Article  Google Scholar 

9. Charman, T., Taylor, E., Drew, A., Cockerill, H., Brown, J.A., Baird, G.: Outcome at 7 years of children diagnosed with autism at age 2: predictive validity of assessments conducted at 2 and 3 years of age and pattern of symptom change over time. J. Child Psychol. Psychiatry 46(5), 500–513 (2005)

   Article  Google Scholar 

10. Black, M.P., Bone, D., Williams, M.E., Gorrindo, P., Levitt, P., Narayanan, S: The USC care corpus: child-psychologist interactions of children with autism spectrum disorders. In: Proceedings of Interspeech 2011, pp. 1497–1500. ISCA, Florence, Italy (2011)

    Google Scholar 

11. Lord, C., Rutter, M., DiLavore, P.C., Risi, S., Gotham, K., Bishop, S.: Autism Diagnostic Observation Schedule: ADOS, 2nd edn. Western Psychological Services, Torrance, CA (2012)

    Google Scholar 

12. Kuijper, S.J., Hartman, C.A., Hendriks, P.: Who is he? Children with ASD and ADHD take the listener into account in their production of ambiguous pronouns. PLoS ONE 10(7), e0132408 (2015)

    Google Scholar 

13. Gale, R., Chen, L., Dolata, J., Van Santen, J., Asgari, M.: Improving ASR systems for children with autism and language impairment using domain-focused DNN transfer techniques. In: Proceedings of Interspeech 2019, pp. 11–15. ISCA, Graz, Austria (2019)

    Google Scholar 

14. O’Sullivan, J., et al.: Automatic speech recognition for ASD using the open-source whisper model from OpenAI. In: International Society for autism Research (INSAR) 2023 Annual Meeting. INSAR, Stockholm, Sweden (2023)

    Google Scholar 

15. Cho, S., Liberman, M., Ryant, N., Cola, M., Schultz, R.T., Parish-Morris, J.: Automatic detection of autism spectrum disorder in children using acoustic and text features from brief natural conversations. In: Proceedings of Interspeech 2019, pp. 2513–2517. ISCA, Graz, Austria (2019)

    Google Scholar 

16. Ashwini, B., Narayan, V., Shukla, J.: SPASHT: semantic and pragmatic speech features for automatic assessment of autism. In: Proceedings of 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5. IEEE, Rhodes Island, Greece (2023)

    Google Scholar 

17. Boughattas, N., Jabnoun, H.: Autism spectrum disorder (ASD) detection using machine learning algorithms. In: Aloulou, H., Abdulrazak, B., de Marassé-Enouf, A., Mokhtari, M. (eds.) Participative Urban Health and Healthy Aging in the Age of AI. ICOST 2022. LNCS, vol. 13287, pp. 225–233. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-09593-1_18

18. Farooq, M.S., Tehseen, R., Sabir, M., Atal, Z.: Detection of autism spectrum disorder (ASD) in children and adults using machine learning. Sci. Rep. 13, 9605 (2023)

    Google Scholar 

19. Zhao, Z., Tang, H., Zhang, X., Qu, X., Hu, X., Lu, J.: Classification of children with autism and typical development using eye-tracking data from face-to-face conversations: machine learning model development and performance evaluation. J. Med. Internet Res. 23(8), e29328 (2021)

    Google Scholar 

20. Chen, C. P., Gau, S. S. F., Lee, C. C.: Learning converse-level multimodal embedding to assess social deficit severity for autism spectrum disorder. In: Proceedings of 2020 IEEE International Conference on Multimedia and Expo, pp. 1–6. London, U.K. (2020)

    Google Scholar 

21. Wolk, L., Brennan, C.: Phonological investigation of speech sound errors in children with autism spectrum disorders. Speech Lang. Hear. 16(4), 239–246 (2013)

    Article  Google Scholar 

22. Ringeval, F., et al.: Automatic intonation recognition for the prosodic assessment of language-impaired children. IEEE Trans. Audio Speech Lang. Process. 19(5), 1328–1342 (2010)

    Google Scholar 

23. Schopler, E., Van Bourgondien, M.E., Wellman, G.J., Love, S.R.: Childhood Autism Rating Scale: CARS, 2nd edn. Western Psychological Services, Los Angeles (2010)

    Google Scholar 

24. Lee, S., Mun, J., Kim, S., Chung, M.: Speech corpus for Korean children with autism spectrum disorder: towards automatic assessment systems. In: Proceedings of LREC-COLING 2024, pp. 15160–15170. ELRA and ICCL, Torino, Italy (2024)

    Google Scholar 

25. Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision, In: International Conference on Machine Learning, pp. 28492–28518. PMLR (2023)

    Google Scholar 

26. Conneau, A., Baevski, A., Collobert, R., Mohamed, A., Auli, M.: Unsupervised cross-lingual representation learning for speech recognition. In: Proceedings of Interspeech 2021, pp. 2426–2430. ISCA, Brno, Czech Republic (2021)

    Google Scholar 

Download references