论文标题
用于访问控制的文本独立扬声器标识系统
Text Independent Speaker Identification System for Access Control
论文作者
论文摘要
甚至人类智能系统也无法提供100％的准确性来识别特定个人的演讲。 Machine Intelligence试图通过各种语音提取和语音建模技术来模仿说话者识别问题。本文提出了一种独立于文本的扬声器识别系统，该系统采用了MEL频率sepstral系数（MFCC）进行特征提取和K-Nearest邻居（KNN）进行分类。获得的最大交叉验证精度为60％。随后的研究将得到改善。
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.
