Abstract
Acoustic analysis is a proper method in vocal fold pathology diagnosis so that it can complement and in some cases replace the other invasive, based on direct vocal fold observation, methods. There are different approaches for vocal fold pathology diagnosis. These algorithms usually have two stages which are Feature Extraction and Classification. While the second stage implies a choice of a variety of machine learning methods, the first stage plays a critical role in performance of the classification system. In this paper, three types of features which are Energy and Entropy resulting from the Wavelet Packet Tree and Mel-Frequency-Cepstral-Coefficients (MFCCs), and also their combination are investigated. Finally a new type of feature vector, based on Energy and Mel-Frequency-Cepstral-Coefficients, is proposed. Support vector machine is used as a classifier for evaluating the performance of our proposed method. The results show the priority of the proposed method in comparison with other methods.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Alonso, J.B., Leon, J.D., Alonso, I., Ferrer, M.A.: Automatic Detection of Pathologies in the Voice by HOS Based Parameters. EURASIP Journal on Applied Signal Processing 2001(4), 275–284 (2001)
Ceballos, L.G., Hansen, J., Kaiser, J.: A Non-Linear Based Speech Feature Analysis Method with Application to Vocal Fold Pathology Assessment. IEEE Trans. Biomedical Engineering 45(3), 300–313 (2005)
Ceballos, L.G., Hansen, J., Kaiser, J.: Vocal Fold Pathology Assessment Using AM Autocorrelation Analysis of the Teager Energy Operator. In: Proc. of the ICSLP 1996, pp. 757–760 (1996)
Adnene, C., Lamia, B.: Analysis of Pathological Voices by Speech Processing. In: 2003 Proc. of the Signal Processing and Its Applications, vol. 1(1), pp. 365–367 (2003)
Manfredi, C.: Adaptive Noise Energy Estimation in Pathological Speech Signals. IEEE Trans. Biomedical Engineering 47(11), 1538–1543 (2000)
Llorente, J.I.G., Vilda, P.G.: Automatic Detection of Voice Impairments by Means of Short-Term Cepstral Parameters and Neural Network Based Detectors. IEEE Trans. Biomedical Engineering 51(2), 380–384 (2004)
Rosa, M.D.O., Pereira, J.C., Grellet, M.: Adaptive Estimation of Residue Signal for Voice Pathology Diagnosis. IEEE Trans. Biomedical Engineering 47(1), 96–104 (2000)
Mallat, S.G.: A Theory for Multi-resolution Signal Decomposition: the Wavelet Representation. IEEE Trans. Pattern Analysis and Machine Intelligence 11(7), 674–693 (1989)
Wallen, E.J., Hansen, J.H.: A Screening Test for Speech Pathology Assessment Using Objective Quality Measures. In: Proc. of the ICSLP 1996, pp. 776–779 (1996)
Chen, W., Peng, C., Zhu, X., Wan, B., Wei, D.: SVM-based identification of pathological voices. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS, pp. 3786–3789 (2007)
Ritchings, R.T., McGillion, M.A., Moore, C.J.: Pathological voice quality assessment using artificial neural networks. Medical Engineering & Physics 24(8), 561–564 (2002)
Lee, J.-Y., Jeong, S., Hahn, M.: Classification of pathological and normal voice based on linear Discriminant analysis. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007. LNCS, vol. 4432, pp. 382–390. Springer, Heidelberg (2007)
Herisa, H.K., Aghazadeh, B.S., Bahrami, M.N.: Optimal feature selection for the assessment of vocal fold disorders. Computers in Biology and Medicine 39(10), 860–868 (2009)
Fonseca, E.S., Guido, R.C., Scalassarsa, P.R., Maciel, C.D., Pereira, J.C.: Wavelet time frequency analysis and least squares support vector machines for identification of voice disorders. Computers in Biology and Medicine 37(4), 571–578 (2007)
Guido, R.C., Pereira, J.C., Fonseca, E.S., Sanchez, F.L., Vierira, L.S.: Trying different wavelets on the search for voice disorders sorting. In: Proceedings of the 37th IEEE International Southeastern Symposium on System Theory, pp. 495–499 (2005)
Umapathy, K., Krishnan, S.: Feature analysis of pathological speech signals using local discriminant bases technique. Medical and Biological Engineering and Computing 43(4), 457–464 (2005)
Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)
Li, T., Oginara, M., Li, Q.: A comparative study on content based music genre classification. In: Proc. of the 26th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 282–289 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Majidnezhad, V., Kheidorov, I. (2013). A Novel Method for Feature Extraction in Vocal Fold Pathology Diagnosis. In: Godara, B., Nikita, K.S. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 61. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37893-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-37893-5_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37892-8
Online ISBN: 978-3-642-37893-5
eBook Packages: Computer ScienceComputer Science (R0)