Exploring Speech Disorder Detection in Laryngeal Diseases: A Comprehensive Examination

Speech disorders due to laryngeal diseases can significantly impact an individual’s quality of life, leading to difficulties in communication and social interaction. Early detection and diagnosis of these disorders is crucial for effective treatment and management. Deep learning algorithms have shown promise in detecting speech disorders using acoustic features extracted from speech signals in recent years. This paper presents a review of the current state-of-the-art in the detection of speech disorders due to laryngeal diseases using deep learning algorithms. The review covers recent research on various deep learning techniques, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and their variants. The paper also discusses the challenges and limitations of using deep learning to detect speech disorders, such as the need for large training data and the potential for overfitting. Strategies for addressing these challenges, such as transfer learning and data augmentation, are also discussed. The review concludes by highlighting the potential of deep learning algorithms for the early detection and diagnosis of speech disorders due to laryngeal diseases. The study suggests that further research is needed to validate the effectiveness of deep learning algorithms in real-world clinical settings and to develop robust and reliable diagnostic tools for detecting speech disorders. The paper highlights the importance of collaboration between researchers and clinicians to translate deep learning research into clinical practice successfully.