Text Independent Speaker Identification System for Access Control
Audio and Speech Processing
2022-09-30 v1 Machine Learning
Sound
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.
Cite
@article{arxiv.2209.14335,
title = {Text Independent Speaker Identification System for Access Control},
author = {Oluyemi E. Adetoyi},
journal= {arXiv preprint arXiv:2209.14335},
year = {2022}
}
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4 pages