Performance of Text-Independent Automatic Speaker Recognition on a Multicore System
作者机构:Faculty of Tech and Software EngineeringUniversity of Europe for Applied SciencesPotsdam 14469Germany
出 版 物:《Tsinghua Science and Technology》 (清华大学学报(自然科学版(英文版))
年 卷 期:2024年第29卷第2期
页 面:447-456页
核心收录:
学科分类:12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 080902[工学-电路与系统] 0809[工学-电子科学与技术(可授工学、理学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术(可授工学、理学学位)]
主 题:Automatic Speaker Recognition(ASR) Gaussian Mixture Model(GMM) shared memory parallel programming PThreads OpenMP
摘 要:This paper studies a high-speed text-independent Automatic Speaker Recognition(ASR)algorithm based on a multicore system s Gaussian Mixture Model(GMM).The high speech is achieved using parallel implementation of the feature s extraction and aggregation methods during training and testing *** memory parallel programming techniques using both OpenMP and PThreads libraries are developed to accelerate the code and improve the performance of the ASR *** experimental results show speed-up improvements of around 3.2 on a personal laptop with Intel i5-6300HQ(2.3 GHz,four cores without hyper-threading,and 8 GB of RAM).In addition,a remarkable 100%speaker recognition accuracy is achieved.