Abstract
In this paper, we propose the use of a novel feature set, i.e., modulation spectrogram for fricative classification. Modulation spectrogram gives 2-dimensional (i.e., 2-D) feature vector for each phoneme. Higher Order Singular Value Decomposition (HOSVD) is used to reduce the size of large dimensional feature vector obtained by modulation spectrogram. These features are then used to classify the fricatives in five broad classes on the basis of place of articulation (viz., labiodental, dental, alveolar, post-alveolar and glottal). Four-fold cross-validation experiments have been conducted on TIMIT database. Our experimental results show 89.09 % and 87.51 % accuracies for recognition of place of articulation of fricatives and phoneme-level fricative classification,respectively, using 3-nearest neighbor classifier.
Chapter PDF
Similar content being viewed by others
Keywords
References
Quatieri, T.F.: Discrete-time Speech Signal Processing: Principles and Practice. Prentice Hall Press, Upper Saddle River (2004)
Web Source, http://www.langsci.ucl.ac.uk/ipa/IPA_chart_C2005.pdf (last accessed on 30th April, 2013)
Garofolo, J.S.: Getting started with the DARPA TIMIT CD-ROM: An acoustic phonetic continuous speech database. National Institute of Standards and Technology (NIST), Gaithersburgh, MD (1988)
Scanlon, P., Ellis, D., Reilly, R.: Using Broad Phonetic Group Experts for Improved Speech Recognition. IEEE Trans. on Audio, Speech and Language Proc. 15, 803–812 (2007)
Ali, A.M.A., Spiegel, J.V., Mueller, P.: Acoustic-phonetic features for automatic classification of fricatives. J. Accoust. Soc. of America 109(5), 2217–2235 (2001)
Ali, A.M.A., Spiegel, J.V., Muller, P.: An acoustic-phonetic feature-based system for the automatic recognition of fricative consonents. In: IEEE Proc. on Int. Conf. on Acoustics, Speech and Signal Processing, vol. 2, pp. 961–964 (1998)
Seneff, S.: A Joint Synchrony/ Mean Rate Model of Auditory Speech Processing. J. Phonetics 16, 55–76 (1988)
Atlas, L., Shamma, A.S.: Joint acoustic and modulation frequency. EURASIP J. on Applied signal Proccessing 7, 668–675 (2003)
Greenberg, S., Kingsbury, B.: The modulation spectrogram: In pursuit of an invariant representation of speech. In: IEEE Proc. on Int. Conf. on Acoust., Speech, Signal Process., Munich, Germany, vol. 3, pp. 1647–1650 (1997)
Markaki, M., Stylianou, Y.: Voice pathology detection and discrimination based on modulation spectral features. IEEE Trans. on Audio, Speech, and Language Proc. 19(7), 1938–1948 (2011)
Lathauwer, L.D., Moor, B.D., Vandewalle, J.: A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21(4), 1253–1278 (2000)
Modulation Toolbox, http://www.ee.washington.edu/research/isdl/projects/modulationtoolbox (last accessed on 30th April 2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Malde, K.D., Chittora, A., Patil, H.A. (2013). Classification of Fricatives Using Novel Modulation Spectrogram Based Features. In: Maji, P., Ghosh, A., Murty, M.N., Ghosh, K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2013. Lecture Notes in Computer Science, vol 8251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45062-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-45062-4_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-45061-7
Online ISBN: 978-3-642-45062-4
eBook Packages: Computer ScienceComputer Science (R0)