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Original Article
Medi Voice – an AI-based Disease Prediction System integrated with Speech Processing
Dr. M. V. Rajesh1
NJS Visali2
K. Kartheek Sai Viswanadh3
Lasya Rani. E4
SJD Prakash5
1 2 3 4 5 Department of Information Technology, Aditya University, Surampalem, Andhra Pradesh, India.
Published Online: March-April 2026
Pages: 12-16
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260702002
References
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Generative Adversarial Neural Network,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 33, pp. 2097–
2111, 2025, doi: 10.1109/TNSRE.2025.3570383.
2. S. R. Shahamiri, “Speech Vision: An End-to-End Deep Learning-Based Dysarthric Automatic Speech Recognition System,” IEEE
Transactions vol. 29, pp. 852–861, 2021, doi: 10.1109/TNSRE.2021.3076778.
3. S. R. Shahamiri, V. Lal and D. Shah, “Dysarthric Speech Transformer: A Sequence-to-Sequence Dysarthric Speech Recognition
System,” IEEE Transactions,vol. 31, pp. 3407–3416, 2023, doi: 10.1109/TNSRE.2023.3307020.
4. S. A. Naeini, L. Simmatis, D. Jafari, Y. Yunusova and B. Taati, “Improving Dysarthric Speech Segmentation With Emulated and
Synthetic Augmentation,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 12, pp. 382–389, 2024, doi:
10.1109/JTEHM.2024.3375323.
5. M. G. Gonzales, P. Corcoran, N. Harte and M. Schukat, “Joint Speech-Text Embeddings for Multitask Speech Processing,” IEEE
Access, vol. 12, pp. 145955–145967, 2024, doi: 10.1109/ACCESS.2024.3473743.
6. A. Asha, B. Dhiyanesh, G. Kiruthiga, L. Shakkeera, V. Jacob and A. Venaik, "Efficient Coronary Heart Disease Prediction: Chaos
Salp Decision with Features Optimization to Neural Network," Int. J. Intell. Syst. Appl. Eng., vol. 12(20s), pp. 440–450, 2024.
7. G.García-, M. T. Bayon-, C. Benavides-, J. Aveleira-, and J. A. Benitez-, J. R. Predicting the Risk of Heart Disease with Deep Learning
Techniques and Feature Augmentation arXiv preprint, Feb. 2024.
8. M. A. Khan, et al., An Automated System of Heart Disease Prediction With the help of the machine learning techniques, IEEE Access,
vol. 8, p. 80235-80245, 2020, doi: 10.1109/ACCESS.2020.2992093.
9. K. Kumar, et al., A Hybrid Framework to predict heart disease with Classical machine learning and Quantum-inspired machine
learning, Scientific Reports, vol. 15, art. 25040, Jul. 2025.
10. M. S. I. Sumon, et al., CardioTabNet: A new hybrid Transformer Model to predict heart diseases with Tabular Medical Data, arXiv
preprint, Mar. 2025.
Generative Adversarial Neural Network,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 33, pp. 2097–
2111, 2025, doi: 10.1109/TNSRE.2025.3570383.
2. S. R. Shahamiri, “Speech Vision: An End-to-End Deep Learning-Based Dysarthric Automatic Speech Recognition System,” IEEE
Transactions vol. 29, pp. 852–861, 2021, doi: 10.1109/TNSRE.2021.3076778.
3. S. R. Shahamiri, V. Lal and D. Shah, “Dysarthric Speech Transformer: A Sequence-to-Sequence Dysarthric Speech Recognition
System,” IEEE Transactions,vol. 31, pp. 3407–3416, 2023, doi: 10.1109/TNSRE.2023.3307020.
4. S. A. Naeini, L. Simmatis, D. Jafari, Y. Yunusova and B. Taati, “Improving Dysarthric Speech Segmentation With Emulated and
Synthetic Augmentation,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 12, pp. 382–389, 2024, doi:
10.1109/JTEHM.2024.3375323.
5. M. G. Gonzales, P. Corcoran, N. Harte and M. Schukat, “Joint Speech-Text Embeddings for Multitask Speech Processing,” IEEE
Access, vol. 12, pp. 145955–145967, 2024, doi: 10.1109/ACCESS.2024.3473743.
6. A. Asha, B. Dhiyanesh, G. Kiruthiga, L. Shakkeera, V. Jacob and A. Venaik, "Efficient Coronary Heart Disease Prediction: Chaos
Salp Decision with Features Optimization to Neural Network," Int. J. Intell. Syst. Appl. Eng., vol. 12(20s), pp. 440–450, 2024.
7. G.García-, M. T. Bayon-, C. Benavides-, J. Aveleira-, and J. A. Benitez-, J. R. Predicting the Risk of Heart Disease with Deep Learning
Techniques and Feature Augmentation arXiv preprint, Feb. 2024.
8. M. A. Khan, et al., An Automated System of Heart Disease Prediction With the help of the machine learning techniques, IEEE Access,
vol. 8, p. 80235-80245, 2020, doi: 10.1109/ACCESS.2020.2992093.
9. K. Kumar, et al., A Hybrid Framework to predict heart disease with Classical machine learning and Quantum-inspired machine
learning, Scientific Reports, vol. 15, art. 25040, Jul. 2025.
10. M. S. I. Sumon, et al., CardioTabNet: A new hybrid Transformer Model to predict heart diseases with Tabular Medical Data, arXiv
preprint, Mar. 2025.
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