Text Independent Speaker Identification System for Access Control
This work addresses speaker identification for access control systems, but it is incremental with limited performance gains.
The paper tackled the problem of text-independent speaker identification for access control by using MFCC for feature extraction and kNN for classification, achieving a maximum cross-validation accuracy of 60%.
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.