Development of Speaker Recognizer Using I-vectors in Two Programming Environments

Maroš Jakubec, Eva Lieskovská, R. Jarina
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Abstract

The i-vectors with Probabilistic Linear Discriminative Analysis (PLDA) are known to be one of the latest and most advanced techniques in the field of Automatic Speaker Recognition (ASR). The paper focuses on the development of i-vector/PLDA based the ASR systems in two programming environments, namely Python and MATLAB, which are popular among machine-learning community. Comparative evaluation of system performance, in terms of accuracy and computational requirements, for both platforms is presented.
两种编程环境下基于i向量的说话人识别器的开发
基于概率线性判别分析(PLDA)的i向量是语音自动识别(ASR)领域最新、最先进的技术之一。本文重点研究了基于i-vector/PLDA的ASR系统在机器学习界流行的两种编程环境(Python和MATLAB)中的开发。从精度和计算需求两方面对两种平台的系统性能进行了比较评价。
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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