【作者】 陈伟；

【导师】 李辉；

【作者基本信息】 中国科学技术大学 ， 电路与系统， 2011， 硕士

【摘要】 随着科学技术的发展,说话人确认技术的研究重点逐渐从实验室环境转向现实生活中的复杂环境。这给说话人确认带来了许多新的亟待解决的问题,信道失配就是其中的一个典型的具有代表性的问题。信道失配,是指训练语音和测试语音分别来自不同的传输信道,进而导致话者确认系统的性能下降。本论文分析了基于GMM-UBM结构和SVM模型的说话人确认系统;探讨了多种在特征域、模型域和得分域中常用的信道失配补偿算法;给出了基于冗余属性投影的信道失配问题的解决方案,并对冗余属性算法进行了优化。本论文主要的研究内容如下:一、深入研究了基于概率统计高斯混合模型-通用背景模型(GMM-UBM)结构的话者确认系统,探讨了EM算法和MAP算法。针对概率统计模型的区分性不足以及区分性辨别模型对说话人个性特征描述不够精确的问题,给出了基于GMM-SVM结构的话者确认系统,利用GMM模型对特征参数进行压缩和聚类后,采用从GMM模型中构建出的GMM-supervector作为SVM的输入来建立目标话者模型。二、深入研究了在复杂信道条件下的说话人确认的失配补偿方法,针对特征参数的倒谱均值减、相关谱滤波、特征映射等方法;针对模型的因子分析等方法;针对测试评分的测试规整等方法。在NIST数据库上的对比试验表明这些方法可以改善信道失配对话者确认系统带来的负面影响。三、深入研究了基于GMM-SVM结构的话者确认系统中的信道失配问题,给出了一种通过消除SVM输入特征GMM-supervector中的信道子空间的成分的失配补偿算法。使用集外大量已知信道类型信息的语音训练出映射矩阵,然后训练语音和测试语音都利用此矩阵进行映射,从而得到受信道影响更小的说话人确认系统。?更多还原

【Abstract】 With the development of science and technology, the research of speaker verification technology is refocused from laboratory environment to the complex environment in real life. It brings about many new problems to be solved. Channel mismatch is one of the typical and representative problems. Channel mismatch that training voices and testing voices from different transmission channels results in the decline of the system of speaker verification.The thesis analyzes two speaker verification systems. One is based on GMM-UBM structure, and the other is based on SVM. It discusses common channel mismatch compensation algorithm of feature, model and score. The solution of the problem is proposed, which is based on nuisance attribute projection (NAP). Then the algorithm is optimized.The main research contents are as follows:Firstly, the thesis discuss the speaker verification system based on probability statistical GMM-UBM. Then EM algorithm and MAP algorithm are discussed deeply. Aim at improving the distinction of probability statistical model system and disposing the problem that the lack of speaker personalized information description of distinguish identify model, the GMM-SVM speaker verification system is proposed. GMM model is used to compress and cluster the feature parameters. GMM-supervector from GMM is used as the input of SVM to set up the speaker model.Secondly, the channel compensation mismatch method of speaker verification is discussed, such as CMS, RASTA, Feature Mapping, Factor Analysis and T-norm. Comparative experiment based on NIST database shows that the methods which are discussed above can improve the negative impacts of speaker verification which is brought about by channel mismatch.Thirdly, the problem of channel mismatch from GMM-SVM speaker verification system is explored deeply. The mismatch compensation algorithm is proposed that it can remove the information of channel of GMM-supervector used as SVM input. Voices from a number of known channels are used to train projected matrix. Then training voices and testing voices are projected using the matrix. Last, the speaker verification system which is suffering little effect from channel appears.更多还原