Strategy for developing a speech recognition model specialized for patients with depression or Parkinson’s disease with small size speech database
Author:
Funder
Inha University
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s13534-024-00389-w.pdf
Reference19 articles.
1. Schultz BG, Tarigoppula VSA, Noffs G, Rojas S, Walt A, Grayden DB, Vogel AP. Automatic speech recognition in neurodegenerative disease. Int J Speech Technol. 2021;24(3):771–9. https://doi.org/10.1007/s10772-021-09836-w.
2. Dubagunta SP, Vlasenko B, Magimai.-Doss M. Learning voice source related information for depression detection. In: ICASSP 2019—2019 IEEE international conference on acoustics, speech and signal processing (ICASSP); 2019. pp. 6525–6529. https://doi.org/10.1109/ICASSP.2019.8683498
3. Alpert M, Pouget ER, Silva RR. Reflections of depression in acoustic measures of the patient’s speech. J Affect Disord. 2001;66(1):59–69. https://doi.org/10.1016/S0165-0327(00)00335-9.
4. Cummins N, Scherer S, Krajewski J, Schnieder S, Epps J, Quatieri TF. A review of depression and suicide risk assessment using speech analysis. Speech Commun. 2015;71:10–49. https://doi.org/10.1016/j.specom.2015.03.004.
5. Caballero-Morales S-O, Trujillo-Romero F. Evolutionary approach for integration of multiple pronunciation patterns for enhancement of dysarthric speech recognition. Expert Syst Appl 2014;41(3), 841–852. https://doi.org/10.1016/j.eswa.2013.08.014 . Methods and Applications of Artificial and Computational Intelligence
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