Abstract
Automatic Speaker Recognition (ASR)is an economic method of biometrics because of the availability of low cost and powerful processors. An ASR system will be efficient if the proper speaker-specific features are extracted. Most of the state-of-the-art ASR systems use the natural speech signal (either read speech or spontaneous speech) from the subjects. In this paper, an attempt is made to identify speakers from their hum. The experiments are shown for Linear Prediction Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC), and Mel Frequency Cepstral Coefficients (MFCC) as input feature vectors to the polynomial classifier of 2nd order approximation. Results are found to be better for MFCC than LP-based features.
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
Adams, N.H., Bartsch, M.A., Wakefield, G.H.: Note Segmentation and Quantization for Music Information Retrieval. IEEE Trans. Audio., Speech and Language Processing 14(1), 131–141 (2006)
Atal, B.S., Hanuaer, S.L.: Speech analysis and synthesis by linear prediction of the speech wave. J. Acoust. Soc. Amer. 50, 637–655 (1971)
Atal, B.S.: Effectiveness of linear prediction of the speech wave for automatic speaker identification and verification. J. Acoust. Soc. Amer. 55, 1304–1312 (1974)
Campbell, W.M., Assaleh, K.T., Broun, C.C.: Speaker recognition with polynomial classifiers. IEEE Trans. on Speech and Audio Processing 10, 205–212 (2002)
Davis, S.B., Mermelstein, P.: Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans. Acoust., Speech and Signal Processing 28, 357–366 (1980)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification and Scene Analysis, 2nd edn. Wiley-Interscience, Chichester (2001)
Jang, J.-S.R., Lee, H.-R.: A General Framework of Progressive Filtering and Its Application to Query by Singing/Humming. IEEE Trans. Audio., Speech and Language Processing 16(2), 350–358 (2008)
Kersta, L.G.: Voiceprint Identification. Nature 196, 1253–1257 (1962)
Oppenheim, A.V., Schafer, R.W.: Discrete-Time Signal Processing. Prentice-Hall, Englewood Cliffs (1989)
Patil, H.A.: Speaker Recognition in Indian Languages: A Feature Based Approach. Ph.D. thesis, Department of Electrical Engineering, IIT Kharagpur, India (2005)
Yegnanarayana, B., Prasanna, S.R.M., Zachariah, J.M., Gupta, C.S.: Combining evidence from source, suprasegmental and spectral features for a fixed-text speaker verification system. IEEE Trans. Speech Audio Processing 13(4), 575–582 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Patil, H.A., Jain, R., Jain, P. (2008). Identification of Speakers from Their Hum. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2008. Lecture Notes in Computer Science(), vol 5246. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87391-4_59
Download citation
DOI: https://doi.org/10.1007/978-3-540-87391-4_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87390-7
Online ISBN: 978-3-540-87391-4
eBook Packages: Computer ScienceComputer Science (R0)