Applicaton of parameters of voice singal autoregressive models to solve speaker recognition problems

Evgeny Novikov, Vladimir Trubitsyn

Abstract


 AbstractAn approach to the formation of the voice signal (VS) informative features of the Vietnamese language on the basis of stationary autoregressive model coefficients is described. An original algorithm of VS segmentation based on interval estimation of speech sample numerical characteristics was developed to form local stationarity areas of the voice signal. The peculiarity is the use of high order autoregressive coefficients, the set of which is determined on the basis of discriminant analysis.

Tóm tắt— Bài báo xem xét cách hình thành các đặc tính thông tin tín hiệu thoại của tiếng Việt trên cơ sở mô hình hệ số tự hồi quy cố định. Một thuật toán nguyên thủy cho việc phân chia tín hiệu thoại trên cơ sở ước lượng khoảng cách của các đặc điểm của số mẫu giọng nói đã được phát triển, để hình thành các vùng cố định cục bộ của tín hiệu thoại. Điểm đặc biệt trong cách tiếp cận là sử dụng các hệ số tự hồi quy bậc cao, mà tập của chúng được xác định trên một cơ sở của các phân tích phân biệt.

 


Keywords


voice signal, voice signal segmentation, informative speech features, autoregression models; discriminant data analysis.

Full Text:

PDF

References


[1]. Sorokin V. N. “Voice recognition: analytical review” V. N. Sorokin, V. V. Vyugin, A. A. Tananykin, Information processes,.Vol. 12, No. 1. pp. 1–13, 2012.

[2]. Pervushin E. A. “Review of the main methods of speaker recognition” / E. A. Pervushin // Mathematical structures and modeling,, No. 3 (24), pp. 41–54, 2011.

[3]. I. Rohmanenko. “Algorithms and software for verifying an announcer using an arbitrary phrase: thesis ... cand. tech. sciences”. [Electronic resource]. URL: https://postgraduate.tusur.ru /system/file_copies/ files / 000/000/262 / original / dissertation.pdf Tomsk, , 111 pp. 2017.

[4]. Ahmad K. S. A “unique approach in text independent speaker recognition using MFCC feature sets and probabilistic neural network” // Advances in Pattern Recognition (ICAPR), Eighth International Conference on..,pp.16. 2015

[5]. Markel, J. D. Linear Prediction of Speech: [trans. from English.] / J. D. Markel, A. H. Gray; under the editorship of Yu.N. Prokhorov and V. S. Zvezdin. – Moscow: Communication, 308 p,1980.

[6]. Lysak A. B. Identification and authentication of a person: a review of the basic biometric methods of user authentication of computer systems / A. B. Lysak // Mathematical structures and modeling.. No. 2 (26). – pp. 124–134,2012.

[7]. Meshcheryakov R. V. Algorithms for evaluating automatic segmentation of a speech signal / R. V. Meshcheryakov, A. A. Konev // Informatics and Control Systems.– No. 1 (31). – pp. 195–206. 2012.

[8]. Ding J., Yen C. T. Enhancing GMM speaker identification by incorporating SVM speaker verification for intelligent web-based speech applications // Multimedia Tools and Applications.. – Vol. 74. – No. 14. – pp. 5131-5140, 2015.

[9]. Trubitsyn VG Models and algorithms in speech signal analysis systems: dis. ... cand. tech. sciences. – Belgorod, 2013 .– 134 pp. [Electronic resource]. URL: http://dissercat.com/content/modeli-i-algoritmy-v-sistemakh-analiza-rechevykh-signalov.

[10]. Ganapathiraju A., Hamaker J., Picone J., Doddington G.R. and Ordowski M. Syllable-Based Large Vocabulary Continuous Speech Recognition. IEEE Transactions on Speech and Audio Processing, Vol. 9, No. 4, pp. 358–366, 2001.

[11]. Tomchuk K. K. Segmentation of speech signals for tasks of automatic speech processing: dis. cand. tech. sciences. – St. Petersburg.– 197 pp. [Electronic resource]. URL: http://fs.guap.ru/dissov/tomchuk_kk/full.pdf, 2017.

[12]. Sorokin V. N. Segmentation and recognition of vowels / V. N. Sorokin, A. I. Tsyplikhin // Information Processes. Vol . 4. – No. 2. – pp. 202–220, 2004.

[13]. Nguyen An Tuan Automatic analysis, recognition and synthesis of tonal speech (based on the material of the Vietnamese language): dissertation ... Doctors of technical sciences. – Moscow– 456 pp. [Electronic resource]. URL: https: // dissercat.com/content/avtomaticheskii-analiz-raspoznavanie-i-sintez-tonalnoi-rechi-na-materiale-vetnamskogo-yazyka, 1984.

[14]. Gmurman V. Ye. Probability theory and mathematical statistics: textbook. manual for universities / V. E. Gmurman. – 12th ed., Revised. – M.: Yurayt, 2010 .– 478 p.

[15]. Boxing J., Jenkins G. Time Series Analysis / Per. from English; Ed. V.F. Pisarenko. M .: Mir, 1974.– 406 pp.

[16]. Kantorovich, G. G. Analysis of time series // Moscow, 2003. – 129 pp. [Electronic resource]. URL: http: // biznesbooks.com/components/com_jshopping/files / demo_products / kantorovich-g-g-analiz-vremennykh-ryadov.pdf.

[17]. Novikov E.I. Parameterization of a speech signal based on autoregressive models / E.I. Novikov, Do Kao Khan, // XI All-Russian Interdepartmental Scientific Conference "Actual problems of the development of security systems, special communications and information for the needs of public authorities of the Russian Federation Federations”: materials and reports (Oryol, February 5-6, 2019). At 10 hours / under the general editorship of P. L. Malyshev. – Eagle: Academy of the Federal Security Service of Russia, –pp. 127–130, 2019..

[18]. Kim J.-O. Factor, discriminant and cluster analysis: Per. from English / J.-O. Kim, C.W. Muller, W.R. Kleck and others; Ed. I.S. Enyukova. – M.: Finance and Statistics, – 215 pp, 1989.


Refbacks

  • There are currently no refbacks.