An Overview of ML Techniques for Timely Recognition of Voice Maladies

Humans have an astounding ability to control loudness and tone through their complex voice production system. The vocal cords are vulnerable to damage from both internal and external factors, which can alter a person's voice. Both the physiological and psychological aspects of the body are impacted by these changes. To help patients cope with potential consequences and enhance their quality of life, it is crucial to quickly identify any changes in voice quality. Considering that this substantially improves our understanding of these disorders, it is imperative that machine learning methodologies be applied to automatically diagnose voice abnormalities. Computational strategies to aid clinicians in the early diagnosis of speech anomalies have been the attention of many investigations in recent years. An exhaustive assessment of previous work in the area is the goal of this project, which will target in on automated voice disorder identification and how efficiently it can distinguish between different kinds of vocal impairments. The investigation entails looking into different databases, feature extraction procedures, and ML approaches that have been executed in these studies.