Graphics Processing Unit (GPU) based Optimized Linear Predictive Coding (LPC) Feature Extraction Algorithm for Automatic Speech Recognition using Compute Unified Device Architecture (CUDA)

Abstract

Recent studies have shown a lot of interest in the field of Automatic Speech Recognition (ASR), and its application. ASR is a technology/process of taking spoken words as input through a speech recognition system or application, and converting/translating them into written text as output. Whilst the study of ASR has attracted a lot of research lately, accuracy in the field of speech recognition remains a great challenge, as relevant features of speech need to be extracted for processing by the speech recognition system. Of particular concern is feature extraction as the most critical phase of automatic speech recognition. This is the process of obtaining the most relevant information from the original (i.e., input speech) data and representing that information in a lower data rate. ASR systems must be accurate in their processes of recognizing speech. In that regard, different approaches as an effort to improve the accuracy of ASR systems exist. This work implemented an optimized Linear Predictive Coding (LPC) feature extraction technique to acquire efficient extraction of relevant features during this critical phase. The algorithm was implemented on a Graphics Processing Unit (GPU) integrated system using a Compute Unified Device Architecture (CUDA). Experimental results have shown improvement by this version of the LPC algorithm. Achieved results added up to 10 per cent overall performance improvement in reference to the achieved original LPC results, on which the CPU optimized LPC brought in 6 per cent performance improvement.