  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.
