基于极端梯度增强分类器的语音紊乱自动检测系统

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI:10.1109/IRASET52964.2022.9737980

Mohammed Saad Darouiche, Hicham El Moubtahij, Majid Ben Yakhlef, El Bachir Tazi

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引用次数: 1

摘要

本文介绍了我们开发的语音障碍自动检测(AVDD)系统的方法，该系统可以诊断患者的语音，并确定语音是正常健康的还是由于某种病理导致的紊乱。我们的系统基于Saarbrucken语音数据集(SVD)来提供我们的机器学习模型，并利用Mel频率倒谱系数(MFCC)作为提取的特征。对于分类，我们选择了极端梯度增强(XGBoost)分类器。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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An Automatic Voice Disorder Detection System Based On Extreme Gradient Boosting Classifier

This paper describes our approach to develop an Automatic Voice Disorder Detection (AVDD) system that can diagnose the patient voice and determine if the voice is normal and healthy or disordered due to a certain pathology. Our system is based on the Saarbrucken Voice Dataset (SVD) to feed our machine-learning model, and exploiting the Mel Frequency Cepstral Coefficients (MFCC) as an extracted feature. For the classification, we chose the Extreme Gradient Boosting (XGBoost) classifier.

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2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)

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