Robust and Complex Approach of Pathological Speech Signal Analysis

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.

arXiv comment41 pages, published in Neurocomputing, Volume 167, 2015, Pages 94-111, ISSN 0925-2312

References in corpus (2)

• Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection
• The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures

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