Research on Pathological Voice Recognition Based on XGBoost

Summary

Here, we present a protocol for establishing a non-invasive XGBoost-based AI model for diagnosing vocal cord polyps using the Saarbrücken database.

Abstract

With the continuous growth of human social communication, the number of people suffering from voice disorders is also increasing. Due to the objective and non-invasive advantages of acoustic detection methods for pathological voice, the use of speech signal analysis for pathological voice recognition has become a research hotspot. This article first selected 101 continuous vowels /a/ from the German SVD database as the research object. Secondly, using wavelet packet technology for time-frequency analysis, four nonlinear dynamic parameters, namely approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, are extracted from the sub signals as the feature parameter set for the pathological voice classifier. Finally, the machine learning algorithm XGBoost is selected as the pattern recognition method to establish a pathological voice classifier, and the classification performance is verified using five fold cross validation and ROC curve. Experimental results have shown that the accuracy of XGBoost's pathological voice classifier is 0.857, the F1 score is 0.875, and the AUC value is 0.944, all of which are higher than the classifier constructed by SVM, indicating that XGBoost has better performance in pathological voice recognition.

Introduction

The number of people suffering from voice disorders is constantly increasing. According to statistics, one-third of the population experiences voice problems at some point in their lives¹. For professional voice users such as singers and teachers, due to their frequent misuse and abuse of their voice, the incidence of throat diseases is higher. Two studies conducted surveys on teachers in Tianjin and Urumqi, and the results showed that the probability of suffering from voice disorders is 33.81% and 28.23%, respectively^2,3. As a result, there is increasing emphasis on the detection and d....

Protocol

The technical workflow of this study is illustrated in Figure 1.

1. Acquisition of voice samples

1. Source the experimental data from the SVD in Germany¹².
2. Obtain 101 cases of vowel /a/ voice data, including 45 cases (19 males and 26 females) of vocal polyp patients and 56 cases (28 males and 28 females) of normal individuals.

2. Wavelet packet decomposition of voice data¹³

1. Conduct an exhaustive search across three Daubechies wavelets (db1, 3, 5) and three decompos....

Results

In this study, wavelet packet analysis was employed to perform time-frequency decomposition of voice signals. By integrating nonlinear dynamic parameters—including approximate entropy, sample entropy, fuzzy entropy, and permutation entropy—the complexity and irregularity characteristics of pathological voices associated with vocal fold polyps were systematically characterized. Two pathological voice classifiers were subsequently constructed based on the support vector machine (SVM) algorithm and the XGBoost algorithm, re.......

Discussion

Pathological voice diagnosis utilizing speech signal analysis represents a non-invasive and objective approach¹⁹. Consequently, pathological voice classification has emerged as a significant research focus in speech signal processing and recognition, demonstrating substantial clinical application value and developmental prospects²⁰. This study employed speech samples collected from SVD as experimental corpora. Through speech signal processing and machine learning techniques.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
MATLAB	The MathWorks	R2023a	Primary software platform for numerical computation and algorithm prototyping.
Python software	Python Software Foundation	Python 3.10	Core programming language used throughout the study.
Saarbruecken Voice Database,SVD	Institute of Phonetics, Saarland University	Saarbrücken Voice Database (SVD)	Publicly available database of pathological and normal voice recordings. Contains sustained vowels and continuous speech from subjects with conditions including vocal cord polyps, as well as healthy controls. Used for academic research under its specified terms of access.