基于CNN的语言文本独立小型系统说话人识别

Rohan Jagiasi, Shubham Ghosalkar, Punit Kulal, A. Bharambe
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引用次数: 14

摘要

说话人识别是系统从系统中可用的说话人样本中识别说话人的能力。它有两种类型,一种使用关键字,称为文本依赖系统,另一种可以识别任何语言/文本的语音,也称为文本独立说话人识别。本文利用密集卷积神经网络实现了一个文本无关、语言无关的说话人识别系统。说话人识别已经在一些即将问世的电子产品中得到了应用,比如个人/家庭助理、电话银行和生物识别。在本文中,我们探索了一个使用MFCC与DNN和CNN作为模型来构建说话人识别系统的系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CNN based speaker recognition in language and text-independent small scale system
Speaker Recognition is the ability of the system to recognize the speaker from the set of speaker samples available in the system. It is of 2 types, one uses a keyword, called text-dependent systems, and another one can recognize the voice in any language/text, also called as text-independent speaker recognition. In this paper, a text-independent, language-independent speaker recognition system is implemented using dense & convolutional neural networks. Speaker recognition has found several applications in upcoming electronic products like personal/home assistants, telephone banking and biometric identification. In this paper, we explore a system that uses MFCC along with DNN and CNN as the model for building a speaker recognition system.
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