基于段级特征主成分分析的说话人识别算法

doi:10.11772/j.issn.1001-9081.2013.07.1935

计算机应用 ›› 2013, Vol. 33 ›› Issue (07): 1935-1937.DOI: 10.11772/j.issn.1001-9081.2013.07.1935

基于段级特征主成分分析的说话人识别算法

储雯¹,²,李银国²,徐洋²,孟祥涛¹,²

1. 重庆邮电大学计算机科学与技术学院，重庆 400065
2. 重庆邮电大学汽车电子与嵌入式系统工程研究中心，重庆 400065

收稿日期:2013-01-17 修回日期:2013-02-18 出版日期:2013-07-01 发布日期:2013-07-06
通讯作者: 储雯
作者简介:储雯（1985-），女（土家族），重庆人，硕士研究生，主要研究方向：语音识别；李银国（1955-），男，湖北黄梅人，教授，博士生导师，博士，主要研究方向：模式识别、人工智能、系统辨识与智能控制；徐洋（1977-），男，重庆人，副教授，博士研究生，主要研究方向：仪器仪表、嵌入式数字系统。
基金资助:
重庆市科委自然科学基金资助项目（cstc2012jjA60002）

Speaker recognition method based on utterance level principal component analysis

CHU Wen¹,²,LI Yinguo²,XU Yang²,MENG Xiangtao¹,²

1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. Research Center of Automotive Electronics and Embedded System Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2013-01-17 Revised:2013-02-18 Online:2013-07-06 Published:2013-07-01
Contact: CHU Wen

摘要/Abstract

摘要： 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。

关键词: 说话人识别, 非线性分段, 主成分分析, 说话人识别系统

Abstract: 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.

Key words: Speaker Recognition (SR), non-linear partition, Principal Component Analysis (PCA), speaker recognition system

中图分类号:

TP18

储雯李银国徐洋孟祥涛. 基于段级特征主成分分析的说话人识别算法[J]. 计算机应用, 2013, 33(07): 1935-1937.

CHU Wen LI Yinguo XU Yang MENG Xiangtao. Speaker recognition method based on utterance level principal component analysis[J]. Journal of Computer Applications, 2013, 33(07): 1935-1937.

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基于段级特征主成分分析的说话人识别算法

Speaker recognition method based on utterance level principal component analysis

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