Performance of Text-Independent Automatic Speaker Recognition on a Multicore System

Abstract

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 procedures. Shared memory parallel programming techniques using both OpenMP and PThreads libraries are developed to accelerate the code and improve the performance of the ASR algorithm. The 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.