Speaker Recognition: A Comparative analysis Between Deep Learning and Non-Deep Learning Methodologies

Abstract

This work carries out a comparative study of two methodologies for speaker recognition. It is Deep Learning (DL) and Vector Quantization (VQ). The key area in biometric au-thentication systems involves speaker recognition. This requires durable and efficient algorithms. The aim is to ensure a high accuracy and reliability. The study delves into a deep neural network (DNN) model implementation. It leverages advanced feature extraction. It uses pattern recognition capabilities which are in DL. The study also examines a traditional VQ approach. This method makes use of codebook generation and quantization for speaker ID. Extensive experimentation was done on standard datasets. The project evaluates the performance of two methods. It compares accuracy. It assesses computational complexity. It does so for noise and for variations in speech. The findings of this analysis reveal the strengths and limitations of each technique. Looking at their practical applicability in real-world scenarios provides insights. The comparative results of these techniques aim to guide future developments. This concerns speaker recognition systems - particularly their potential for enhanced performance.