Abstract
In the present work an assessmet of the influence of the different components that form a bioinspired auditory model in the speaker recognition performance by means of neuronal networks, at different sound pressure levels and Gaussian white noise of the voice signal, was made. The speaker voice is processed through three variants of an auditory model. From its output, a set of psychophysical parameters is extracted, with which neuronal networks for speaker recognition will be trained. Furthermore, the aim is to compare three standardization methods of parameters. As a conclusion, we can observed how psycophysical parameters characterize the speaker with acceptable rates of recognition; the typology of auditory model has influence on speaker recognition.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Resch, B.: Automatic Speech Recognition with HTK (A Tutorial for the Course Computational Intelligence), pp. 1–6 (2004)
Richardson, M., Bilmes, J., Diorio, C.: Hidden-Articulator Markov Models for Speech Recognition. Speech Communication 41, 511–529 (2003)
Reynolds, D.A.: Automatic Speaker Recognition, pp. 1–42. MIT Lincoln Laboratory (2002)
Wan, V.: Speaker Verification using Support Vector Machines (2003)
Godino–Llorente, J.I., Aguilera–Navarro, S., Gómez–Vilda, P.: Detección automática de patología por abuso vocal mediante modelos estadísticos de mezclas Gaussianas. In: URSI, pp. 1–2 (2001)
Reynolds, D.A., Rose, R.C.: Robust text-independent speaker identification using Gaussian Mixture Speaker Models. IEEE Transactions on Speech and Audio Processing 3(1), 72–83 (1995)
Reynolds, D.A.: Speaker identification and verification using Gaussian mixture speaker models. Speech Communication, 91–108 (1995)
Ganapathiraju, A.: Support vector machines for speech recognition (2002)
Clarkson, P., Moreno, P.J.: On the use of support vector machines for phonetic classification, 1–4 (2005)
Chen, P., Lin, C., Schölkopf, B.: A Tutorial on v-Support Vector Machines, 1–29 (2003)
Martínez–Rams, E.A., Garcerán–Hernández, V.: Assessment of a speaker recognition system based on an auditory model and neural nets. In: Mira, J., Ferrández, J.M., Álvarez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2009. LNCS, vol. 5602, pp. 488–498. Springer, Heidelberg (2009)
Martínez–Rams, E., Cano–Ortiz, S.D., Garcerán–Hernández, V.: Implantes Cocleares: Desarrollo y Perspectivas. Revista Mexicana de Ingeniería Biomédica 27(1), 45–54 (2006)
Martínez–Rams, E., Garcerán–Hernández, V., Ferrández–Vicente, J.M.: Low Rate Stochastic Strategy for Cochlear Implants. Neurocomputing Letters 72(4-6), 936–943 (2009)
Lopez Poveda, E.A., Eustaquio-Martín, A.: A biophysical model of the Inner Hair Cell: The contribution of potassium currents to peripherical auditory compression. Journal of the Association for Research in Otolaryngology JARO 7, 218–235 (2006)
Ortega-Garcia, J., González-Rodriguez, J., Marrero-Aguiar, V.: Ahumada: A large speech corpus in Spanish for speaker identification and verification. Speech Communication 31(2-3), 255–264 (2004)
Martínez–Rams, E., Cano–Ortiz, S.D., Garcerán–Hernández, V.: Diseño de banco de filtros para modelar la membrana basilar en una prótesis coclear. In: Conferencia Internacional FIE, pp. 1–6 (2006)
Lopez-Poveda, E.A., Meddis, R.: A human nonlinear cochlear filterbank. J. Acoust. Soc. Am. 110(6), 3107–3118 (2001)
Martínez–Rams, E., Garcerán–Hernández, V.: ANF Stochastic Low Rate Stimulation. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2007. LNCS, vol. 4527, pp. 103–112. Springer, Heidelberg (2007)
Martínez–Rams, E., Garcerán–Hernández, V.: A Speaker Recognition System based on an Auditory Model and Neural Nets: performance at different levels of Sound Pressure and of Gaussian White Noise. In: Ferrández, J.M., et al. (eds.) IWINAC 2011. LNCS, vol. 6687, pp. 157–166. Springer, Heidelberg (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martínez–Rams, E.A., Garcerán–Hernández, V. (2011). Speaker Recognition Based on a Bio-inspired Auditory Model: Influence of Its Components, Sound Pressure and Noise Level. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) New Challenges on Bioinspired Applications. IWINAC 2011. Lecture Notes in Computer Science, vol 6687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21326-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-21326-7_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21325-0
Online ISBN: 978-3-642-21326-7
eBook Packages: Computer ScienceComputer Science (R0)