To improve the calculation speed and robustness of the Speaker Recognition (SR) system,the authors proposed a speaker recognition algorithm method based on utterance level Principal Component Analysis (PCA),which was derived from the frame level features.Instead of frame level features,this algorithm used the utterance level features in both training and recognition.What's more,the PCA method was also used for dimension reduction and redundancy removing.The experimental results show that this algorithm not only gets a little higher recognition rate,but also suppresses the effect of the noise on speaker recognition system.It verifies that the algorithm based on utterance level features PCA can get faster recognition speed and higher system recognition rate,and it enhances system recognition rate in different noise environments under different Signal-to-Noise Ratio (SNR) conditions.%为了提高说话人识别(SR)系统的运算速度,增强其鲁棒性,以现有的帧级语音特征为基础,提出了一种基于段级特征主成分分析的说话人识别算法.该算法在训练和识别阶段以段级特征代替帧级特征,然后用主成分分析方法对段级特征进行降维、去相关.实验结果表明,该算法的系统训练时间、测试时间分别为基线系统的47.8％、40.0％,同时识别率略有提高,抑制了噪声对说话人识别系统的影响.该结果验证了基于段级特征主成分分析的说话人识别算法在识别率有所提高的情况下取得了较快的识别速度,同时在不同噪声环境下的不同信噪比情况下均可以提高系统识别率.
展开▼
