Multi Tasking Synthetic Speech Detection on Indian Languages

A. R. Ambili, Rajesh Cherian Roy
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引用次数: 3

Abstract

Anti-spoofing research plays an important role in audio forensics. It has found a lot of traction in several languages around the world. With that in mind, the purpose of this work is to assess the impact of several synthetic spoofing detection approaches on a multilingual, low-constrained Indian language set. This paper aims at a multitasking spoofing detection by identifying real/spoof utterance identification as well as the regional language spoofing attack vector. To accomplish this, the features and the classifiers that are best candidate for the synthetic spoofing detection and language identification are appropriately chosen. Our methodology compares the performances of three main different classifiers GMM, SVM, DNN on the vector formulated from the accumulation of MFCC features. Hindi, Malayalam, Tamil, Telugu are the four languages which are taken into account for the comparison. Out of these classifiers, SVM and DNN are found to give the best results with EER rates of 1.98 % and 1.19 % respectively.
印度语言的多任务合成语音检测
反欺骗研究在音频取证中占有重要地位。它在世界各地的几种语言中都很受欢迎。考虑到这一点,这项工作的目的是评估几种综合欺骗检测方法对多语言、低约束印度语言集的影响。本文旨在通过识别真实/欺骗的话语识别以及区域语言欺骗攻击向量来实现多任务欺骗检测。为了实现这一点,需要适当地选择合成欺骗检测和语言识别的最佳候选特征和分类器。我们的方法比较了三种主要不同分类器GMM, SVM, DNN在由MFCC特征累积而成的向量上的性能。印地语、马拉雅拉姆语、泰米尔语、泰卢固语是四种被考虑到比较的语言。在这些分类器中,SVM和DNN的EER率分别为1.98%和1.19%,结果最好。
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