Robust and Complex Approach of Pathological Speech Signal Analysis

TL;DR

This paper reviews state-of-the-art methods in pathological speech signal analysis, introduces 36 new features from diverse signal processing domains, and evaluates their effectiveness across multiple languages and databases.

Contribution

It introduces 36 novel pathological voice measures derived from advanced signal processing techniques, enhancing feature sets for speech pathology analysis.

Findings

New features improve classification accuracy

Features show high sensitivity and specificity

Effective across multiple languages

Abstract

This paper presents a study of the approaches in the state-of-the-art in the field of pathological speech signal analysis with a special focus on parametrization techniques. It provides a description of 92 speech features where some of them are already widely used in this field of science and some of them have not been tried yet (they come from different areas of speech signal processing like speech recognition or coding). As an original contribution, this work introduces 36 completely new pathological voice measures based on modulation spectra, inferior colliculus coefficients, bicepstrum, sample and approximate entropy and empirical mode decomposition. The significance of these features was tested on 3 (English, Spanish and Czech) pathological voice databases with respect to classification accuracy, sensitivity and specificity.