计算机应用论文说话人特征提取和识别算法的研究

1、说话人特征提取和识别算法的研究 说话人特征提取和识别算法的研究 research of speaker feature extraction and recognition algorithm【中文摘要】 说话人识别以其独特的方便性、经济性和准确性等优势在公安、司法、声控以及军事等领域有着广阔的应用前景。如何从大量的语音数据中提取反映说话人个性特征的语音参数和设计行之有效的分类器是说话人识别领域研究的热点。针对支持向量机法(support vector machine,svm)和核fisher判别方法(kernel fisher discriminant,kfd)存在的问题,本文提出了自己的解

2、决方案。1.针对说话人识别中语音数据维数高和识别模型训练慢的问题,提出基于主分量空间的pca分类方法和基于截断误差空间的pca分类方法,在保证识别性能的情况下,可以有效地降低后续阶段的计算复杂度。2.针对svm不能直接处理动态时间序列语音数据的问题,提出一种基于添加原始分类信息fisher分值法(aoi-fisher score)的序列特征提取方法。为了解决aoi-fisher分值法计算复杂度高的问题,采用pca对mfcc参数进行降维的同时筛选出可能的目标说话人,缩小说话人的范围,减少输入样本的数量。该方法可以提高svm在整体语音数据上进行分类的性能,同时又可降低系统的建模时间。3.提出一种基

3、于tes-pca和核fisher判别(kfd)的多级说话人确认方法。pca分类器实现简单,可以快速的判断出可能的目标说话人,然后采用kfd进行最终的说话人确认。该方法克服了kfd的计算复杂度随着样本规模的增大而提高的缺陷。相比于传统的svm分类器和kfd分类器,仿真实验结果表明多级分类器具有较高的识别率,同时在建模时间和识别速度方面占有较大的优势。【英文摘要】 due to its special merits of flexibility, economy and accuracy, speaker recognition was widely applied in the field of

4、 public security, judicature, voice-controlled cryptography, criminal verification, patient verification and military affairs. the research hotspots of speaker recognition are in two aspects: one is how to extract the effective speech parameter from large-scale speech data; the other is the design o

5、f effective classifer.based on the problems existed in svm and kfd, we proposed some novel and improved methods. the research work of this paper is as follows.1. based on the deeply research of optimal dimension reduction and classify in pca, we proposed the pcs-pca classifier and tes-pca classifier

6、 to solve the problem of high dimension and time-consuming model training existed in speaker recognition. this approach could consumedly reduce the computation complexity of later stage.2. aimming at the problem of svm that it could not process dynamic time sequence directly, add original informatio

7、n fisher score (aoi-fisher score) was proposed to achieve sequence features. for the sake of reducing the compute complexity of aoi-fisher score, pca was utilized to reduce the dimensions of mfcc, and possible target speakers were selected synchronously. so the range of registered speaker was shorte

8、ned and the number of input vectors was reduced. svm could classify the whole sequence by this method; meanwhile the computation complexity was reduced.3. a hierarchical speaker verification method based on pca classifier and kernel fisher discriminant was proposed. using the simpleness and fast cla

9、ssification of pca, the dimensions of registered speakers feature vectors was reduced, and possible target speakers were fast selected simultaneously. then, kfd was used to make final decision. this method overcomed the limitation of kfd in which the compute complexity was enhanced along with the au

10、gment of speech data. compared with conventional svm and kfd, the experiment results showed that our proposed hierarchical classifier has superior performance, and this method is in the ascendant of recognition rate and model time.【中文关键词】 说话人识别; 支持向量机; fisher分值; 核fisher判别; pca分类器 【英文关键词】 speaker rec

11、ognition; support vector machine; fisher score; kernel fisher discriminant; pca classifier 【毕业论文目录】毕业论文论文摘要 7-8 abstract 8 插图索引 9-10 附表索引 10-11 第1章 绪论 11-16 1.1 课题研究背景及意义 11-12 1.2 说话人识别研究现状 12-14 1.2.1 技术研究现状 12-13 1.2.2 应用研究现状 13-14 1.3 说话人识别研究难点 14 1.4 本文的主要工作及内容结构安排 14-16 第2章 说话人识别技术

12、 16-31 2.1 说话人识别原理 16-18 2.2 特征参数的预处理及特征提取 18-23 2.2.1 语音信号的预处理 18-20 2.2.2 常用特征参数 20-22 2.2.3 主分量分析降维去噪 22-23 2.3 说话人识别模型 23-30 2.3.1 矢量量化模型 23-24 2.3.2 隐马尔科夫模型 24-25 2.3.3 高斯混合模型 25-26 2.3.4 人工神经网络法 26-27 2.3.5 支持向量机法 27-30 2.3.6 混合模型法 30 2.4 本章小结 30-31 第3章 基于pcs-pca和aoi-fisher分值的序列特征提取 31-39 3.1 基于gmm模型的fisher分值法 32-33 3.2 基于pcs-pca和aoi-fisher分值的svm说话人确认 33-36 3.2.1 pcs-pca降维筛选 34 3.2.2 aoi-fisher分值定长转换 34-35 3.2.3 支持向量机判决 35-36 3.3 仿真实验结果及分析 36-38 3.3.1 语音库及预处理 36 3.3.2 实验结果及分析 36-38 3.4 本章小结 38-39 第4章 基于tes-pca分类器和kfd的多级说话人确认