Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (12): 343-348.doi: 10.11896/jsjkx.210100038

• Information Security • Previous Articles     Next Articles

Identification Method of Voiceprint Identity Based on MFCC Features

WANG Xue-guang1, ZHU Jun-wen1, ZHANG Ai-xin2   

  1. 1 Criminal Justice College,East China University of Political Science and Law,Shanghai 200052,China
    2 School of Cyber Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China
  • Received:2021-01-06 Revised:2021-04-23 Online:2021-12-15 Published:2021-11-26
  • About author:WANG Xue-guang,born in 1975,Ph.D,professor,is a member of China Computer Federation.His main research interests include computer networks,big data application and electronic data.
  • Supported by:
    National Key R & D Program of China(2017YFB0802103).

PDF (PC)

863

Abstract: As a product of the development of modern forensic technology,voiceprint plays an important role in modern audio-visual identification.The traditional voiceprint analysis method is based on the sound processing tools for manual analysis.Considering the shortcomings of strict text relevance and conjecture of comparison,its evidential power as evidence appraisal opinion needs to be strengthened.In this paper,a method of identification based on Mel frequency cepstrum coefficient is proposed,which is to extract and quantify the envelope containing the original sound formant and its time axis information as voiceprint features for identity comparison.This method improves the shortcomings of traditional Mel frequency cepstrum coefficient,which extracts the mutation of formant,and adds the transformation characteristics of vowels and consonants into voiceprint features to improve the correctness of recognition.Experiments show that the accuracy of identification is 85% and the variance is about 9% when the test material is independent of the sample text.Therefore,it has good identifiability for the same person identification of voiceprint.In the case of non same person identification of voiceprint,it proves to be far more accurate in combination with traditional manual analysis.

Key words: Identity identification, Mel frequency cepstrum coefficient, MFCC characteristics, Proof force reinforcement

CLC Number: 

  • TP391
[1]ZHAO Y Y.The confusion and solution of audio-visual mate- rials,electronic data and electronic evidence－ from the perspective of information electronic technology[J].Journal of South China University of Technology(Social Science Edition),2020(2):1-10.
[2]KANG J T,WANG L,WANG X D,et al.A Review on Researches of Forensic Phonetics and Acoustics in 2017[J].Forensic Science and Technology,2018,43(3):179-186.
[3]ZHOU Y Y,KONG Q.Research of Feature Parameters in Voiceprint Recognition Technology Based on GMM-UBM[J].Computer Technology and Development,2020(5):1-11.
[4]LI W P.A study on the application of rank-sum test for comprehensive evaluation of acoustic pattern identification[J].Journal of Criminal Investigation Police University of China,1994(3):62-64.
[5]PETER F,CAO H L,LEI Y J.A developmental history of forensic speaker comparison in the UK[J].Evidence Science,2019,27(6):730-740.
[6]WANG Z N,CHEN Y,WU M H,et al.Acoustic Analysis of Mandarin Chinese Vowels Produced by Young Adults[J].Rehabilitation Medicine,2020,30(3):183-191.
[7]YU Y S.Research on Pornographic Audio Detection Algorithm Using MFCC Features and Vector Quantization[D].Lanzhou:Lanzhou University,2015.
[8]BRUMMUND M K,SGARD F,PETIT Y,et al.Three-dimensional finite element modeling of the human external ear:Simulation study of the bone conduction occlusion effecta[J].Journal of the Acoustical Society of America,2014,135(3):1433-1444.
[9]WU J Q,YU J J.An improved arithmetic of MFCC in speech recognition system[C]//2011 International Conference on Electronics,Communications and Control(ICECC).IEEE,2011:719-722.
[10]HIDAYAT R,BEJO A,SUMARYONO S,et al.Denoising Speech for MFCC Feature Extraction Using Wavelet Transformation in Speech Recognition System[C]//2018 10th International Conference on Information Technology and Electrical Engineering(ICITEE).2018:280-284.
[11]PETERSON G E,BARNEY H L.Control methods used in a study of the vowels[J].J.Acoust.Soc.Am.,1952,24(2):175-184.
[12]PETERSON G E.Parameters of vowel quality[J].J. of Speech & Hear. Res.,1961,4(1):10-29.
[13]STRANGE W.Evolving theories of vowel perception[J].J.Acoust.Soc.Am.,1989,85(5):2081-2087.
[14]VIJAYAN A,MATHAI B M,VALSALAN K,et al.Throat microphone speech recognition using mfcc[C]//2017 International Conference on Networks & Advances in Computational Technologies(NetACT).2017:392-395.
[15]YANG J B,XING Y L,CAO T Y,et al.Research on Speaker-Independent Speech Recognition Feature Based on Mellin Transform and Mel Frequency Analysis[J].Pattern Recognition and Artificial Intelligence,2020,18(3):350-353.
[16]MESEGUER N A.Speech analysis for automatic speech recognition[D].Trondheim:Norwegian University of Science and Technology,2009.
[17]WINURSITO A,HIDAYAT R,BEJO A.Improvement of MFCC feature extraction accuracy using PCA in Indonesian speech recognition[C]//2018 International Conference on Information and Communications Technology(ICOIACT).2018:379-383.
[18]RIGAZIO L,JUNQUA J,WELLEKENS C.Fundamentals of Speech Recognition[J].AT&T,1993:507.
[19]SENTHILDEVI K A,CHANDRA E.Keyword spotting system for Tamil isolated words using Multidimensional MFCC and DTW algorithm[C]//International Conference on Communications & Signal Processing.IEEE,2015:550-554.
[20]MAHESHA P,VINOD D S.LP-Hillbert transform based MFCC for effective discrimination of stuttering dysfluencies[C]//International Conference on Wireless Communications.2017:2561-2565.
[21]LI Q,YANG Y,LAN T,et al.MSP-MFCC:Energy-Efficient MFCC Feature Extraction Method with Mixed-Signal Proces-sing Architecture for Wearable Speech Recognition Applications[J].IEEE Access,2020(8):48720-48730.
[1] WANG Xue-guang, ZHU Jun-wen, ZHANG Ai-xin. Identification Method of Voiceprint Identity Based on ARIMA Prediction of MFCC Features [J]. Computer Science, 2022, 49(5): 92-97.
[2] CHEN Yan-wen,LI Kun,HAN Yan,WANG Yan-ping. Musical Note Recognition of Musical Instruments Based on MFCC and Constant Q Transform [J]. Computer Science, 2020, 47(3): 149-155.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.