基于量子隧穿效应的说话人真伪鉴别方法

doi:10.11772/j.issn.1001-9081.2017.09.2617

计算机应用 ›› 2017, Vol. 37 ›› Issue (9): 2617-2620.DOI: 10.11772/j.issn.1001-9081.2017.09.2617

基于量子隧穿效应的说话人真伪鉴别方法

黄亮, 潘平, 周超

贵州大学计算机科学与技术学院, 贵阳 550025

收稿日期:2017-03-08 修回日期:2017-07-05 出版日期:2017-09-10 发布日期:2017-09-13
通讯作者: 潘平,panping_17@163.com
作者简介:黄亮(1993-),男,四川资阳人,硕士研究生,主要研究方向:信息与通信系统安全;潘平(1962-),男(苗族),贵州贵阳人,副教授,主要研究方向:信息安全、语音信号处理;周超(1994-),男(穿青人),贵州贵阳人,硕士研究生,主要研究方向:信息与通信系统安全。
基金资助:
贵州省科学技术基金资助项目（黔科合J字[2012]2132号）；贵州省教育厅自然科学研究项目（黔教合KY字（2015）367号）；贵州大学计算机科学与技术学院研究生创新基金资助项目（院创201703）。

Speaker authentication method based on quantum tunneling effect

HUANG Liang, PAN Ping, ZHOU Chao

College of Computer Science and Technology, Guizhou University, Guiyang Guizhou 550025, China

Received:2017-03-08 Revised:2017-07-05 Online:2017-09-10 Published:2017-09-13
Supported by:
This work is partially supported by the Guizhou Provincial Science and Technology Fund ([2012]2132), the Natural Science Research Project of Education Department of Guizhou Province ((2015)367), the Graduate Innovation Fund of College of Computer Science and Technology, Guizhou University (201703).

摘要/Abstract

摘要： 针对语音信号的非结构化特点，提出了一种基于量子隧穿效应的说话人真伪鉴别方法。以量子隧穿效应为理论依据，首先，在分析语音信号分帧的量子特性基础上，将每一帧语音信号看作一个量子态，实现算法的量子化；然后，利用势垒能分离能量的特性，通过构建势垒组以提取信号的能量谱特征，并以此作为特征参数；最后，通过高斯混合模型（GMM）进行语音信号建模，完成说话人的真伪鉴别。仿真结果表明，相对于传统方法，利用量子隧穿效应理论实现说话人鉴别可以有效降低算法的复杂度，提高识别的识别率和可靠性，为量子信息理论和说话人真伪鉴别方法提供了新的研究途径。

关键词: 说话人, 真伪鉴别, 量子隧穿效应, 高斯混合模型

Abstract: Aiming at the unstructured characteristics of speech signal, a method of speaker authentication based on quantum tunneling effect was proposed. Based on quantum tunneling effect, the quantum properties of speech signal framing analyzed, and each speech signal frame was regarded as a quantum state, and the quantization of the algorithm was realized. And then the potential barrier was used to separate the energy characteristics. The barrier group was constructed to extract the energy spectrum characteristics of the signal and used it as the characteristic parameter. The speech signal modeling was finally carried out by the Gaussian Mixture Model (GMM) to complete the authentication of the speaker. The simulation results show that compared with the traditional method, the use of quantum tunneling theory to achieve speaker identification can reduce the complexity of algorithm effectively, improve the discrimination and provide a new direction for speaker authentication and quantum information theory.

Key words: speaker, authentication, quantum tunneling effect, Gaussian Mixture Model (GMM)

中图分类号:

TN912.34

黄亮, 潘平, 周超. 基于量子隧穿效应的说话人真伪鉴别方法[J]. 计算机应用, 2017, 37(9): 2617-2620.

HUANG Liang, PAN Ping, ZHOU Chao. Speaker authentication method based on quantum tunneling effect[J]. Journal of Computer Applications, 2017, 37(9): 2617-2620.

参考文献

[1] HUANG J, SCHULTHEISS P M. Block quantization of correlated Gaussian random variables[J]. IEEE Transactions on Communication Systems, 1963, 11(3):289-296.
[2] LEVINSON S E, RABINER L R, SONDHI M M. An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition[J]. Bell System Technical Journal, 1983, 62(4):1035-1074.
[3] RABINER L R. A tutorial on hidden Markov models, selected applications in speech recognition[J]. Proceedings of the IEEE, 1989, 77(2):257-286.
[4] WAIBEL A, HANAZAWA T, HINTON G, et al. Phoneme recognition using time-delay neural networks[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989, 37(3):328-339.
[5] REYNOLDS D A, ROSE R C. Robust text-independent speaker identification using Guassian mixture speaker models[J]. IEEE Transactions on Speech and Audio Processing, 1995, 3(1):72-83.
[6] 郭春霞.基于MFCC的说话人识别系统研究[D].西安:西安电子科技大学, 2006.(GUO C X. The study of speaker recognition system based on MFCC[D]. Xi'an:Xidian University, 2006.)
[7] 潘平,罗辉,李换双. 基于随机共振的语音真实性检测方法[J].计算机工程,2013,39(5):277-279.(PAN P, LUO H, LI H S. Voice authenticity detection method based on stochastic resonance[J]. Computer Engineering, 2013, 39(5):277-279.)
[8] GRIFFITHS D J. Introduction to Quantum Mechanics[M]. 北京:机械工业出版社, 2012.(GRIFFITHS D J. Introduction to Quantum Mechanics[M]. Beijing:China Machine Press, 2012.)
[9] 田庚,潘平,刘琦.基于量子遂穿效应的数字录音资料真实性鉴别[J].电脑知识与技术,2016,12(6):197-200.(TIAN G, PAN P, LIU Q. The authenticity of digital recording data identification based on quantum tunnelling effect[J]. Computer Knowledge and Technology, 2016, 12(6):197-200.)
[10] 余华,方棣棠,李树青,等.基于正态分布假设的非特定人语音识别[EB/OL].[2017-01-06]. http://cpfd.cnki.com.cn/Article/CPFDTOTAL-SEER199410001045.htm. (YU H, FANG D T, LI S Q, et al. Speech recognition based on normal distribution hypothesis[EB/OL].[2017-01-06]. http://cpfd.cnki.com.cn/Article/CPFDTOTAL-SEER199410001045.htm.)
[11] 何朝霞,潘平,罗辉.基于非线性共振的说话人特征提取研究与仿真[J].科学技术与工程,2012,12(25):6507-6510.(HE Z X, PAN P, LUO H. Research and emulation of speaker feature parameter extraction based on nonlinear resonances[J]. Science Technology and Engineering, 2012, 12(25):6507-6510.)
[12] 潘平,何朝霞.基于duffing随机共振的说话人特征提取方法[J].计算机工程与应用,2012,48(35):123-125.(PAN P, HE Z X. Method of speaker feature parameter extraction based on duffing stochastic resonance[J]. Computer Engineering and Applications, 2012, 48(35):123-125.)
[13] 赵力.语音信号处理[M].北京:机械工业出版社,2003:26-30.(ZHAO L. Speech Signal Processing[M]. Beijing:China Machine Press, 2003:26-30.)

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基于量子隧穿效应的说话人真伪鉴别方法

Speaker authentication method based on quantum tunneling effect

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