Text Independent Speaker Identification System for Access Control

TL;DR

This paper introduces a text-independent speaker identification system using MFCC features and kNN classifier, achieving up to 60% accuracy, aiming for future improvements.

Contribution

It presents a novel combination of MFCC and kNN for speaker identification, with initial accuracy results and a plan for future enhancement.

Findings

Maximum cross-validation accuracy of 60%

MFCC effectively extracts speaker features

kNN provides a baseline classifier

Abstract

Even human intelligence system fails to offer 100% accuracy in identifying speeches from a specific individual. Machine intelligence is trying to mimic humans in speaker identification problems through various approaches to speech feature extraction and speech modeling techniques. This paper presents a text-independent speaker identification system that employs Mel Frequency Cepstral Coefficients (MFCC) for feature extraction and k-Nearest Neighbor (kNN) for classification. The maximum cross-validation accuracy obtained was 60%. This will be improved upon in subsequent research.