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arXiv · 2209.14335

Text Independent Speaker Identification System for Access Control

Oluyemi E. Adetoyi
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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.

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Oluyemi E. Adetoyi. 2022-09-26. Text Independent Speaker Identification System for Access Control. https://arxiv.org/abs/2209.14335

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