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.

翻译：即使人类情报系统也未能提供100%的准确性来辨别某个特定个人的发言。机器情报机构正试图通过各种语言特征提取和语音模型技术,在语音识别问题中模仿人。本文介绍了一种依赖文字的语音识别系统,该系统使用Mel River Cepstraal Covalys(MFCC)进行地物提取和k-Nearest Neghbor(kNN)进行分类。获得的最大交叉验证精确度是60%。在随后的研究中,这将得到改进。