Ahmed Yousef

Ahmed Yousef

Ph.D. Student

Department
  • Communicative Sciences & Disorders
yousefah@msu.edu

Bio

Ahmed is a dual PhD candidate in the departments of Communicative Science & Disorders and Mechanical Engineering at Michigan State University, under the supervision of Dr. Naghibolhosseini. He has a research interest in contributing to a deeper understanding of the voice production mechanisms in connected speech. I employ high-speed videoendoscopy (HSV),

image and signal analysis, biomechanical modeling, and machine learning to study the vocal mechanisms in normal individuals and patients with adductor spasmodic dysphonia (adSD). By incorporating the prior techniques, Ahmed designed and developed automated analysis to extract HSV-based measures and biomarkers of voice production in these patients, which can facilitate accurate diagnosis and effective treatment of this disorder. Ahmed has a quantitative background as he holds a BSc and MSc in Mechanical engineering, which he intends to use while pursuing his future research in speech science. Ahmed finds this work to be rewarding in being able to do research in an area that will have a positive impact on the quality of life of people with voice disorders.

Related Work

Publications

Yousef, A. M., Deliyski, D. D., Zacharias, S. R., & Naghibolhosseini, M. (2022). Deep-Learning-Based Representation of Vocal Fold Dynamics in Adductor Spasmodic Dysphonia during Connected Speech in High-Speed Videoendoscopy. Journal of Voice (in press). https://doi.org/10.1016/j.jvoice.2022.08.022

Yousef, A. M., Deliyski, D. D., Zacharias, S. R., de Alarcon, A., Orlikoff, R. F., & Naghibolhosseini, M. (2022). A Deep Learning Approach for Quantifying Vocal Fold Dynamics During Connected Speech Using Laryngeal High-Speed Videoendoscopy. Journal of Speech, Language, and Hearing Research, 1-16. https://doi.org/10.1044/2022_JSLHR-21-00540

Yousef, A. M., Deliyski, D. D., Zacharias, S. R., & Naghibolhosseini, M. (2022). Detection of vocal fold image obstructions in high-speed videoendoscopy during connected speech in adductor spasmodic dysphonia: A convolutional neural networks approach. Journal of Voice. https://doi.org/10.1016/j.jvoice.2022.01.028

Yousef, A. M., Deliyski, D. D., Zacharias, S. R., de Alarcon, A., Orlikoff, R. F., & Naghibolhosseini, M. (2021). A hybrid machine-learning-based method for analytic representation of the vocal fold edges during connected speech. Applied Sciences, 11(3), 1179. https://doi.org/10.3390/app11031179

Yousef, A. M., Deliyski, D. D., Zacharias, S. R., de Alarcon, A., Orlikoff, R. F., & Naghibolhosseini, M. (2020). Spatial segmentation for laryngeal high-speed videoendoscopy in connected speech. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.017

Contact Information

ORCID iD: https://orcid.org/0000-0003-4038-1138