全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于总体变化子空间自适应的i-vector说话人识别系统研究

DOI: 10.3724/SP.J.1004.2014.01836, PP. 1836-1840

Keywords: 身份认证矢量,总体变化子空间,自适应,说话人识别

Full-Text   Cite this paper   Add to My Lib

Abstract:

?在说话人识别研究中,基于身份认证矢量(identityvector,i-vector)的子空间建模被证明是目前最前沿最有效的说话人建模技术,其中如何有效准确地估计总体变化子空间矩阵T成为影响系统性能好坏的关键问题.本文针对i-vector技术如何在新的应用环境下进行总体变化子空间矩阵T的自适应估计问题进行了研究,并提出了两种行之有效的自适应估计算法.在由美国国家标准技术局(AmericanNationalInstituteofStandardandTechnology,NIST)组织的2008年说话人识别核心评测数据库以及自行采集的测试数据库上的实验结果显示,不论采用测试集数据本身还是与测试集较匹配的开发集数据,通过本文所提的自适应算法来更新总体变化子空间矩阵均可以使更新后的子空间更有利于新测试数据下的低维子空间描述,在新的测试环境下都更有利于说话人分类.此外实验结果还表明基于多子空间拼接的子空间自适应方法性能明显优于迭代自适应方法,而且两者的结合可达到最优的识别性能,且此时利用开发集数据进行自适应可以接近其利用测试集数据进行自适应得到的最优性能.

References

[1]  Li Zhi-Yi, He Liang, Zhang Wei-Qiang, Liu Jia. Speaker recognition based on discriminant i-vector local distance preserving projection. Journal of Tsinghua University (Science and Technology), 2012, 52(5): 598-601 (栗志意, 何亮, 张卫强, 刘加. 基于鉴别性i-vector局部距离保持映射的说话人识别. 清华大学学报(自然科学版), 2012, 52(5): 598-601)
[2]  Campbell W M, Campbell J P, Reynolds D A, Singer E, Torres-Carrasquillo P A. Support vector machines for speaker and language recognition. Computer Speech and Language, 2006, 20(2-3): 210-229
[3]  Kenny P, Boulianne G, Ouellet P, Dumouchel P. Speaker and session variability in GMM-based speaker verification. IEEE Transactions on Audio, Speech and Language Processing, 2007, 15(4): 1448-1460
[4]  Kenny P, Boulianne G, Dumouchel P. Eigenvoice modeling with sparse training data. IEEE Transactions on Audio, Speech, and Language Processing, 2005, 13(3): 345-354
[5]  Dehak N, Kenny P, Ouellet P, Dumouchel P. Front-end factor analysis for speaker verification. IEEE Transactions on Audio, Speech and Language Processing, 2011, 19(4): 788-798
[6]  Kenny P, Boulianne G, Ouellet P, Dumouchel P. Joint factor analysis versus eigenchannels in speaker recognition. IEEE Transactions on Audio, Speech and Language Processing, 2007, 15(4): 1435-1447
[7]  Reynolds D A, Quatieri T F, Dunn R B. Speaker verification using adapted Gaussian mixture models. Digital Signal Processing, 2000, 10(1-3): 19-41
[8]  Cortes C, Vapnik V. Support vector networks. Machine Learning, 1995, 20(3): 273-297
[9]  Zhang Wen-Lin, Zhang Wei-Qiang, Liu Jia, Li Bi-Cheng, Qu Dan. A new subspace based speaker adaptation method. Acta Automatica Sinica, 2011, 37(12): 1495-1502 (张文林, 张卫强, 刘加, 李弼程, 屈丹. 一种新的基于子空间的说话人自适应方法. 自动化学报, 2011, 37(12): 1495-1502)
[10]  Bishop C M. Pattern Recognition and Machine Learning. Berlin: Springer, 2008
[11]  Hatch A O, Kajarekar S, Stolcke A. Within-class covariance normalization for SVM-based speaker recognition. In: Proceedings of the International Conference on Spoken Language Processing. Pittsburgh, PA, 2006. 1471-1474
[12]  He Liang, Shi Yong-Zhe, Liu Jia. Eigenchannel space combination method of joint factor analysis Acta Automatica Sinica, 2011, 37(7): 849-856 (何亮, 史永哲, 刘加. 联合因子分析中的本征信道空间拼接方法. 自动化学报, 2011, 37(7): 849-856)
[13]  Guo Wu, Li Yi-Jie, Dai Li-Rong, Wang Ren-Hua. Factor analysis and space assembling in speaker recognition. Acta Automatica Sinica, 2009, 35(9): 1193-1198 (郭武, 李轶杰, 戴礼荣, 王仁华. 说话人识别中的因子分析以及空间拼接. 自动化学报, 2009, 35(9): 1193-1198)
[14]  Kinnunen T, Li H Z. An overview of text-independent speaker recognition: from features to supervectors. Speech Communication, 2010, 52(1): 12-40

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133