The Voice Biometrics Based on Pitch Replication

Main Article Content

L.C. Moreno
P.B. Lopes


Authentication and security in automated systems have become very much necessary in our days and many techniques have been proposed towards this end. One of these alternatives is biometrics in which human body characteristics are used to authenticate the system user. The objective of this article is to present a method of text independent speaker identification through the replication of pitch characteristics. Pitch is an important speech feature and is used in a variety of applications, including voice biometrics. The proposed method of speaker identification is based on short segments of speech, namely, three seconds for training  and three seconds for the speaker determination. From these segments  pitch characteristics are extracted  and are used in the proposed method of replication for identification of the speaker.


Download data is not yet available.

Article Details

How to Cite
Moreno, L., & Lopes, P. (2018). The Voice Biometrics Based on Pitch Replication. International Journal for Innovation Education and Research, 6(10), 351-358.
Author Biographies

L.C. Moreno, Universidade Presbiteriana Mackenzie

Programa de Pós-Graduação em Engenharia Elétrica e Computação

P.B. Lopes, Universidade Presbiteriana Mackenzie

Programa de Pós-Graduação em Engenharia Elétrica e Computação


[1] CemalHanilçi and FigenErtaş, “Investigation of the effect of data duration and speaker gender on text-independent speaker recognition”, Computer and Electrical Engineering-2012.
[2] Mak MW, Hsiao R, Mak B. A comparison of various adaptation methods for speaker verification with limited enrollment data. In: Proc. ICASSP; 2006. p. 929-32
[3] Vogt R, Sridharan S. “Experiments in session variability modelling for speaker verification”. In: Proc. ICASSP; 2006. p. 897-900
[4] Fauve BGB, Evans NWD, Pearson N, Bonastre JF, Mason JSD. “Influence of task duration in text-independent speaker verification”. In: Proc. interspeech; 2007. p. 794-7.
[5] Vogt R, Baker B, Sridharan S. “Factor analysis subspace estimation for speaker verification with short utterances”. In: Proc. interspeech; 2008. p.853-6.

[6] Vogt R, Lustri C, Sridharan S. “Factor analysis modelling for speaker verification with short utterances”. In: Proc. speaker Odyssey; 2008
[7] Vogt R, Pelecanos JW, Scheffer N, Kajarekar SS, Sridharan S. “Within-session variability modelling for factor analysis speaker verification”. In: Proc. interspeech; 2009. p. 1563-6.
[8] Pelecanos J, Chaudhari U, Ramaswamy G. “Compensation of utterance length for speaker verification”. In: Proc. speaker Odyssey; 2004.
[9] McLaren M, Vogt R, Baker B, Sridharan S. “Experiments in svm-based speaker verification using short utterances”. In: Proc. speaker Odyssey; 2010. p. 83-90.
[10] ArnabPoddar, MdSahidullah, GoutamSaha. “Speaker verification with short utterances: a review of challenges, trends and opportunities”. In: The Institution of Engineering and Techology 2015.
[11] Kinnum,T,Li,H: “An overview of tex-independent speaker recognition from features to supervectores”. Speech Commun, 2010 52(1), pp12-40
[12] Campbell, J.P.Jr: “Speaker recognition a tutorial”, Proc. IEEE, 1997, 85(9) pp 1437-1462
[13] John G. Proakis (Autor),‎ Dimitris G. Manolakis: “Digital Signal Processing”, 4th edition, 2007
[14] Tomi Kinnunen, Haizhou: An Overview of Tex-Independent Speaker Recognition: from Features to Supervectors”, 2011 –
[15] D.A. Reynolds, T.F.Quatieri, and R.B. Dunn.: “Speaker verification using adapted gaussian misture models”. Digital signal processing , vol.10 no.1-3 pp 19-41.2000
[16] D.A. Reynolds and R.C. Rose.: “Robust text-independent speaker identification using gaussian mixture speaker models”. IEEE transactions on speech and audio processing, vol.3 no1 pp-7283, 1995.
[17] Yi-Hsiang Chao, Wei-Ho Tsai and Hsin-Min Wang: ”Improving GMM-UBM speaker verification using discriminate feedback adaptation” Computer Speech&Language, 2009
[18] S.S. Tirumala, R.Wang.: “Speaker Identification Features Extration Methods: A Systematic Review”, An International Journal on Expert Systems with Applications vol.102.July 15,2017
[19] Join Factor Analysis and i-vector Tutorial, disponivel no http:/ 27.03.2018.
[20] M. T. S. Al Kaltakchi and W. L. Woo and S. S. Dlay and J. A. Chamber: Study in Fusion Strategies and Exploiting the Combination of MFCC and PMCC features for Robust Biometric Speaker Identification, 2016.
[21] LeandroA Silva,S. M Peres Sarajane,BoscarioliClodis: Introdução a Mineração de Dados, Brasil, 2016.
[22] Lawrence Rabiner and Biing-Hwang Juang: Fundamentals of Speech Recognition, EUA 1993.
[23] L. Feng: Speaker Recognition Informatics and Mathematical Modelling – Technical Univeristy of Demmark, DTU, ResearchGate, Sep.2004.
[24] X. Sun,:A pitch determination algorithm based on subharmonic-to-harmonic ratio, pp.679-679 -6th Internacional Conference of Spoken Language Processing -China – 2000
[25] Nayana,P,Mathewa,D and Thomasa,A:Comparation of Text Independent Speaker Identification Systems using GMM and i-Vector Methods, 2017
[26] Ruud M. Bolle,Jonathan H. Connel, Sharath Pankanti,Nalini K. Ratha and Andrew W. Senior: Guide to Biometrics, 2004
[27] Garrett Thomas, CS PhD student at Stanford: How does KNN classification compare to classification by neural networks?,2017