Actas de congresos
Analysis Of The Multifractal Nature Of Speech Signals
Registro en:
9783642332746
Lecture Notes In Computer Science (including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics). , v. 7441 LNCS, n. , p. 740 - 748, 2012.
3029743
10.1007/978-3-642-33275-3_91
2-s2.0-84865604777
Autor
Gonzalez D.C.
Luan Ling L.
Violaro F.
Institución
Resumen
Frame duration is an essential parameter to ensure correct application of multifractal signal processing. This paper aims to identify the multifractal nature of speech signals through theoretical study and experimental verification. One important part of this pursuit is to select adequate ranges of frame duration that effectively display evidence of multifractal nature. An overview of multifractal theory is given, including definitions and methods for analyzing and estimating multifractal characteristics and behavior. Based on these methods, we evaluate the utterances from two different Portuguese speech databases by studying their singularity curves (τ(q) and f(α)).We conclude that the frame duration between 50 and 100 ms is more suitable and useful for multifractal speech signal processing in terms of speaker recognition performance [11]. © 2012 Springer-Verlag. 7441 LNCS
740 748 Campell, J., Speaker Recognition: A Tutorial (1998) Proceeding of the IEEE, 85 (9) Reynolds, D.A., Rose, R.C., Robust Text-Independent Speaker Identification Using Mixture Speaker Model (1995) IEEE Trans. Speech Audio Processing, 3 (1), pp. 72-82 Langi, A., Kinsner, W., Consonant Characterization Using Correlation Fractal Dimension for Speech Recognition (1995) Proc. on IEEE Western Canada Conference on Communications, Computer, and Power in the Modem Environment, Winnipeg, Canada, 1, pp. 208-213 Jayant, N., Noll, P., (1984) Digital Coding of Waveforms: Principles and Applications to Speech and Video, p. 688. , Prentice-Hall, Englewood Cliffs Sant'Ana, R., Coelho, R., Alcaim, A., Text-Independent Speaker Recognition Based on the Hurst Parameter and the Multidimensional Fractional Brownian Motion Model (2006) IEEE Trans. on Audio, Speech, and Language Processing, 14 (3), pp. 931-940 Zhou, Y., Wang, J., Zhang, X., Research on Speaker Recognition Based on Multifractal Spectrum Feature (2010) Second International Conference on Computer Modeling and Simulation, pp. 463-466 Maragos, P., Fractal Aspects of Speech Signals: Dimension and Interpolation (1991) Proc. IEEE ICASSP, 1, pp. 417-420 Langitt, A., Soemintapurat, K., Kinsners, W., Multifractal Processing of Speech Signals Information, Communications and Signal Processing (1997) LNCS, 1334, pp. 527-531. , Han, Y., Quing, S. (eds.) ICICS 1997. Springer, Heidelberg Kinsner, W., Grieder, W., Speech Segmentation Using Multifractal Measures and Amplification of Signal Features (2008) Proc. 7th IEEE Int. Conf. on Cognitive Informatics (ICCI 2008), pp. 351-357 Adeyemi, O.A., Multifractal Analysis of Unvoiced Speech Signals (1997) ETD Collection for University of Rhode Island. Paper AAI9805227 González, D.C., Lee, L.L., Violaro, F., (2011) Use of Multifractal Parameters for Speaker Recognition, , M. Eng. thesis, FEEC/UNCAMP, Campinas, Brazil Sténico, J.W., Lee, L.L., (2009) Estimation of Loss Probability and An Admission Control Scheme for Multifractal Network Traffic, , M. Eng. thesis, FEEC/UNCAMP, Campinas, Brazil Riedi, R.H., Crouse, M.S., Ribeiro, V.J., Baraniuk, R.G., A Multifractal Wavelet Model with Application to Network Traffic (1999) IEEE Trans. on Information Theory, 45 (3), pp. 992-1018 Krishna, M.P., Gadre, V.M., Dessay, U.B., (2003) Multifractal Based Network Traffic Modeling, , Kluwer Academic Publishers., Ed. Bombay Ynoguti, C., Violaro, F., (1999) Continuous Speech Recognition Using Hidden Markov Models, , D. Eng. thesis, FEEC/UNCAMP, Campinas, Brazil Holmes, J., Holmes, W., (2001) Speech Synthesis and Recognition, , 2nd edn. Tayor & Francis, London Research Center INRIA Saclay, , http://fraclab.saclay.inria.fr/