This work reviews the advancements in the non-conventional analysis of speech signals,
particularly from an AM-FM analysis point of view. The benefits of such an analysis, as …

Nowadays, the use of mobile devices in the healthcare sector is increasing significantly.
Mobile technologies offer not only forms of communication for multimedia content (eg clinical …

Nowadays, the incidence of voice disorders is increasing rapidly, with about a third of the
population suffering from dysphonia at some point in their lives. Dysphonia is a disorder that …

The intelligent data-driven screening of pathological voice signals is a non-invasive and real-
time tool for computer-aided diagnosis that has attracted increasing attention from …

Automated classification of normal and pathological speech signals can provide an
objective and accurate mechanism for pathological speech diagnosis, and is an active area …

Speech is a natural way of communication used by human beings. It contains linguistic
information like message and paralinguistic information like feelings, speaker's health, and …

This work explores the utility of the Hilbert Spectrum (HS) of the speech signal, constructed
from its AM-FM components, or Intrinsic Mode Functions (IMFs), in characterizing speakers …

This work explores the utility of the time-domain signal components, or the Intrinsic Mode
Functions (IMFs), of speech signals', as generated from the data-adaptive filterbank nature of …

Evaluation results of the developed method are presented. The mean energy value of the
Hilbert spectrum of a speech signal obtained by the complementary ensemble empirical …

The development of non-invasively derived measures for accurately detecting pathologies
that can remove practitioner subjectivity and can save time and cost is an ongoing goal in …