Objective Early recognition and referral are crucial for voice disorder management. Limited
availability of subspecialists, poor primary care awareness, and the need for specialized …

Introduction Over the past decade, several machine learning (ML) algorithms have been
investigated to assess their efficacy in detecting voice disorders. Literature indicates that ML …

Objective Survey the current literature on artificial intelligence (AI) applications for detecting
and classifying vocal pathology using voice recordings, and identify challenges and …

To distinguish pathological voices from healthy voices, automatic voice pathology detection
systems can be built using machine learning (ML) and deep learning (DL) techniques. To …

This article provides a summary of some recent innovations in voice assessment expected to
have an impact in the next 5–10 years on how patients with voice disorders are clinically …

Objectives Diagnostic tools for voice disorders are lacking for primary care physicians.
Artificial intelligence (AI) tools may add to the armamentarium for physicians, decreasing the …

Objectives Advances in artificial intelligence (AI) technology have increased the feasibility of
classifying voice disorders using voice recordings as a screening tool. This work develops …

Automatic objective non-invasive detection of pathological voice based on computerized
analysis of acoustic signals can play an important role in early diagnosis, progression …

Reliable, automatic and objective detector of pathological voice disorders from speech
signals is a long sought-for tool, by voice clinicians as well as by general practitioners. Such …

Objectives Computerized detection of voice disorders has attracted considerable academic
and clinical interest in the hope of providing an effective screening method for voice …