A Robust Voice Pathology Detection System Based on the Combined BiLSTM–CNN Architecture - Rimah Amami; Rim Amami; Chiraz Trabelsi; Sherin Hassan Mabrouk; Hassan A. Khalil (Mendel, 2023)
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.