Voice Signal Analysis with the Application in Biomedicine

Sensor Letters Pub Date : 2020-02-01 DOI:10.1166/sl.2020.4187

Vikas Mittal, R. Sharma

引用次数: 2

Abstract

Voice pathology is the result of improper vocal use. Poor vocal exercise and repeated laryngeal infection may lead to worse voice quality and vocal stresses. This work uses glottal signal parameters obtained from speakers of distinct ages to identify voice disorders. The parameters obtained from the glottal signal, Mel Frequency Cepstrum Coefficients (MFCCs) and combination of glottal and MFFCs are used for pathological voice classification. Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) algorithms are used. Results show that best classification results are achieved using combinations of MFFCs and with glottal parameters including MOQ, which is a novel outcome and most important involvement of this study, with an average efficiency improvement of 3%.

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语音信号分析及其在生物医学中的应用

声音病理是不正确使用声音的结果。不良的声带锻炼和反复的喉部感染可导致较差的音质和声带压力。这项工作使用从不同年龄的说话者获得的声门信号参数来识别声音障碍。从声门信号中获得的参数，Mel频率倒频谱系数(MFCCs)以及声门和MFFCs的组合用于病理语音分类。使用支持向量机(SVM)和k近邻(KNN)算法。结果表明，MFFCs与声门参数(包括MOQ)的组合可以获得最佳的分类结果，这是本研究的一个新结果，也是最重要的参与，平均效率提高了3%。

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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来源期刊

Sensor Letters 工程技术-电化学

自引率

0.00%

发文量

审稿时长

6 months

期刊介绍： The growing interest and activity in the field of sensor technologies requires a forum for rapid dissemination of important results: Sensor Letters is that forum. Sensor Letters offers scientists, engineers and medical experts timely, peer-reviewed research on sensor science and technology of the highest quality. Sensor Letters publish original rapid communications, full papers and timely state-of-the-art reviews encompassing the fundamental and applied research on sensor science and technology in all fields of science, engineering, and medicine. Highest priority will be given to short communications reporting important new scientific and technological findings.