语音认证和说话人验证方法综述

Jayant Gambhir, Vaishali Patil
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引用次数: 0

摘要

本文简要回顾了基于编辑检测和说话人验证的语音认证技术在法医学中的应用。许多过去的研究包括基于ENF、DFT、STFT分析的传统方法用于语音认证,而一些先进的新兴趋势如SVM、Ensemble learning和卷积神经网络被用于语音认证的不同应用。同样,对于说话人验证,研究了LPC、MFCC、i向量、x向量等特征以及神经网络、深度学习等技术。本文从所调查的研究论文中总结了各种实验结果及其优缺点。本综述旨在指导在法医应用中选择最有前途的说话人和语音数据认证方法时做出公正的决定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Review On Speech Authentication And Speaker Verification Methods
This paper gives a brief review of the techniques available for speech authentication based on edit detection and speaker verification related to application in forensics. Many past research includes the traditional methods like ENF, DFT, STFT based analysis for speech authentication while some advanced emerging trends like SVM, Ensemble learning and Convolution Neural network have been employed for speech authentication in different application. Similarly, for speaker verification, features such as LPC, MFCC, i-vector, x-vector and techniques like Neural Network, Deep learning etc have been researched. This paper summaries the various experimental results and their merits and demerits from the research papers surveyed. This review is expected to guide towards making an unbiased decision in choosing most promising method for authentication of Speaker and Speech data in forensic application.
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