A Robust Voice Pathology Detection System Based on the Combined BiLSTM–CNN Architecture

  • Rimah Amami Computer Department, Deanship of Preparatory Year and Supporting Studies, Imam AbdulRahman bin Faisal University, Dammam, KSA
  • Rim Amami Basic Science Department, Deanship of Preparatory Year and Supporting Studies, Imam AbdulRahman bin Faisal University, Dammam, KSA
  • Chiraz Trabelsi Institut Montpellierain Alexander Grothendieck, UMR CNRS 5149, Place Eugene Bataillon, 34090, Montpellier, France
  • Sherin Hassan Mabrouk Self-Development Department, Deanship of Preparatory Year and Supporting Studies, Imam AbdulRahman bin Faisal University, Dammam, KSA
  • Hassan A. Khalil Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
Keywords: Voice Pathology Detection, Convolutional Neural Network, BiLSTM, Hybrid Systems, MEEI Voice Disorders Database


Voice recognition systems have become increasingly important in recent years due to the growing need for more efficient and intuitive human-machine interfaces. The use of Hybrid LSTM networks and deep learning has been very successful in improving speech detection systems. The aim of this paper is to develop a novel approach for the detection of voice pathologies using a hybrid deep learning model that combines the Bidirectional Long Short-Term Memory (BiLSTM) and the Convolutional Neural Network (CNN) architectures. The proposed model uses a combination of temporal and spectral features extracted from speech signals to detect the different types of voice pathologies. The performance of the proposed detection model is evaluated on a publicly available dataset of speech signals from individuals with various voice pathologies(MEEI database). The experimental results showed that the hybrid BiLSTM-CNN model outperforms several classifiers by achieving an accuracy of 98.86\%. The proposed model has the potential to assist health care professionals in the accurate diagnosis and treatment of voice pathologies, and improving the quality of life for affected individuals.


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How to Cite
Amami, R., Amami, R., Trabelsi, C., Mabrouk, S. and Khalil, H. 2023. A Robust Voice Pathology Detection System Based on the Combined BiLSTM–CNN Architecture. MENDEL. 29, 2 (Dec. 2023), 202-210. DOI:https://doi.org/10.13164/mendel.2023.2.202.
Research articles