# Robust and Complex Approach of Pathological Speech Signal Analysis

arXiv: https://arxiv.org/abs/2203.09402 · HTML: https://mlsanity.com/paper/2203.09402/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2022.

## Built on by (most cited first, 5 of 5)

- [Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease](https://mlsanity.com/paper/2411.03044/) (2024)
- [Applications of Entropy in Data Analysis and Machine Learning: A Review](https://mlsanity.com/paper/2503.02921/) (2025)
- [Severity Classification of Parkinson's Disease from Speech using Single Frequency Filtering-based Features](https://mlsanity.com/paper/2308.09042/) (2023)
- [Analysis and Detection of Pathological Voice using Glottal Source Features](https://mlsanity.com/paper/2309.14080/) (2023)
- [Identification of Hypokinetic Dysarthria Using Acoustic Analysis of Poem Recitation](https://mlsanity.com/paper/2203.09880/) (2022)

## Abstract (arXiv)

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.