文章摘要
程小伟,王健,曾庆宁,谢先明,龙超.噪声环境下稳健的说话人识别特征研究[J].声学技术,2017,(5):479~483
噪声环境下稳健的说话人识别特征研究
A study of robust speaker recognition feature under noisy environment
投稿时间:2016-12-06  修订日期:2017-04-01
DOI:10.16300/j.cnki.1000-3630.2017.05.014
中文关键词: 线性预测  正规化  说话人识别  伽马通滤波器组  鲁棒性
英文关键词: linear prediction  regularization  speaker recognition  Gammatone filter bank  robustness
基金项目:国家自然科学基金项目(61461011);教育部重点实验室2016年主任基金项目资助(CRKL160107);广西自然科学基金(2014GXNSFBA118273)项目。
作者单位E-mail
程小伟 桂林电子科技大学信息与通信学院, 广西桂林 541004  
王健 桂林电子科技大学信息与通信学院, 广西桂林 541004  
曾庆宁 桂林电子科技大学信息与通信学院, 广西桂林 541004  
谢先明 桂林电子科技大学信息与通信学院, 广西桂林 541004  
龙超 桂林电子科技大学信息与通信学院, 广西桂林 541004 chengzai05@163.com 
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中文摘要:
      针对噪声环境下说话人识别率较低的问题,提出一种基于正规化线性预测功率谱的说话人识别特征。首先对语音信号线性预测分析和正规化处理求出语音频谱包络,然后通过伽马通滤波器组得到对数子带能量,最后对特征参数进行离散余弦变换,得到了一种说话人识别特征正规化线性预测伽马通滤波器倒谱系数(Regularized Linear Pre-diction Gammatone Filter Cepstral Coefficient,RLP-GFCC)。仿真结果表明,在噪声环境说话人辨认试验中,相比传统特征美尔频率倒谱系数(Mel Frequency Cepstral Coefficient,MFCC)和伽马通滤波器倒谱系数(Gammatone Filter Cepstral Coefficient,GFCC)的系统识别率得到了明显提高,对噪声环境的鲁棒性得到了增强。
英文摘要:
      In order to solve the problem that speaker recognition rate is low under noisy environment, a speaker recognition feature based on regularized linear predictive power spectrum is proposed. The method uses linear prediction analysis and regularization of speech signal to get speech spectral envelope and then to get logarithmic sub-band energy through the Gammatone filter group, and finally uses discrete cosine transform to compute feature parameters to get a kind of speaker recognition feature named regularized linear predicted Gammatone filter cepstral coefficients (RLP-GFCC). The simulation results show that the recognition rate of the system is significantly improved in comparison with the systems of traditional feature MFCC and GFCC under noisy environment, and the robustness of the system to noise environment is improved.
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