Pathological voice characterization has received increasing attention over the last 20 years.
Hundreds of studies have been published showing inventive approaches with very …

Time-frequency analysis methods is an effective tool to analyze non-stationary signals.
Moreover, the utilization of postprocessing algorithms significantly enhances this analytical …

The construction of an automatic voice pathology detection system employing machine
learning algorithms to study voice abnormalities is crucial for the early detection of voice …

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 …

Voice pathology diagnosis requires extracting significant features from voice signals, and
classical machine learning models can overfit to the training data, which can cause difficult …

Automatic voice pathology detection using non-invasive techniques that utilize patients'
speech and electroglottograph (EGG) signals play a vital role in diagnosis and early medical …

Speech is the principal means of communication among humans for centuries. It arises
when the voice tone produced by the vocal cords is modulated by the articulators giving rise …

The purpose of this research was to develop an artificial intelligence-based method for
evaluating substitution voicing (SV) and speech following laryngeal oncosurgery …

In clinical settings, early diagnosis and objective assessment depend on the detection of
voice pathology. To classify anomalous voices, this work uses an approach that combines …

In recent years, deep learning methods in automatic pathological voice detection (APVD)
have gained satisfying results. However, most deep learning methods in APVD cannot …