Acoustic Feature Extraction and Optimized Neural Network Based Classification for Speaker Recognition

Abstract

Identifying the person from his or her voice characteristics is an essential trait for human interaction. Automatic speaker recognition (ASR) systems are developed to find the identity of the speaker in the field of forensics, business interactions and law enforcement. It can be achieved by extracting prosodic, linguistic, and acoustic speech characteristics. Furthermore optimized neural network based approaches are reviewed to classify the extracted features. In this paper, literatures are surveyed on recognition of speaker through the neural network using an optimization algorithm that has developed from the previous years for ASR systems. We deliberate different characteristics of ASR arrangements, containing features, neural network based classification, performance metrics and standard evaluation data sets. ASR system is discussed in two parts. The first part illustrates different feature extraction techniques and the second part involves the classification approaches which identify the speaker. We accomplish this evaluation through a comparative analysis of various recognition of speaker approaches and compare the results of the same.