Gender Determination Using Voice Data

Yavuz Selim Taspinar, M. M. Saritas, Ilkay Cinar, M. Koklu
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引用次数: 1

Abstract

The rapid advancement of today's technologies, it is tried to facilitate whichever system will be used by using voice features such as person recognition and speech recognition by making use of the voices of the users. Organizations serving in these systems need less manpower and facilitate the operation by helping users faster. The decision-making process using sound features is a very challenging process. With gender recognition, which is one of these steps, it is possible to address the user by gender. In this study, it is aimed to define the genders according to the voices in terms of both forensic informatics and the rapid and accurate progress of the processes. In this study, 3168 male and female voice samples were taken as a dataset. Sound samples were first analyzed by acoustic analysis in R using seewave and tuneR packages. Artificial neural networks were used in the classification stage. In order to increase the classification accuracy, the dataset was divided into 10 parts and each part was excluded from training for testing and used for retesting. Average classification success was found by taking the arithmetic mean of the results. In the classification made with artificial neural networks, male and female voices could be distinguished from each other with a success of 97.9%.
利用语音数据确定性别
在技术飞速发展的今天,人们试图通过利用用户的声音,利用人识别、语音识别等语音特征,为将来使用的系统提供便利。在这些系统中服务的组织需要更少的人力,并通过帮助用户更快地方便操作。使用声音功能的决策过程是一个非常具有挑战性的过程。性别识别是其中一个步骤,有了它,就可以按性别来处理用户。在本研究中,它的目的是根据声音来定义性别在法医信息学和过程的快速和准确的进展。本研究选取了3168个男性和女性的语音样本作为数据集。声音样本首先在R中使用seewave和tuneR软件包进行声学分析。分类阶段采用人工神经网络。为了提高分类精度,将数据集分成10个部分,每个部分都从训练中剔除进行测试,用于重新测试。对分类结果取算术平均值,得到分类成功率的平均值。在人工神经网络的分类中,男声和女声的区分成功率为97.9%。
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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