Voice pathology detection on spontaneous speech data using deep learning models
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10772-024-10134-4.pdf
Reference44 articles.
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3. Abdulmajeed, N. Q., Al-Khateeb, B., & Mohammed, M. A. (2023). Voice pathology identification system using a deep learning approach based on unique feature selection sets. Expert Systems. https://doi.org/10.1111/exsy.13327
4. Ali, Z., Alsulaiman, M., Muhammad, G., Elamvazuthi, I., & Mesallam, T. A. (2013). Vocal fold disorder detection based on continuous speech by using MFCC and GMM. In 2013 7th IEEE GCC conference and exhibition (GCC). IEEE.
5. Ali, Z., Elamvazuthi, I., Alsulaiman, M., & Muhammad, G. (2016). Automatic voice pathology detection with running speech by using estimation of auditory spectrum and cepstral coefficients based on the all-pole model. Journal of Voice, 30(6), 757.
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