Abstract
Voice pathologies identification using speech processing methods can be used as a preliminary diagnostic. The aim of this study is to compare the performance of sustained vowel /a/ and continuous speech task in identification systems to diagnose voice pathologies. The system recognizes between three classes consisting of two different pathologies sets and healthy subjects. The signals are evaluated using MFCC (Mel Frequency Cepstral Coefficients) as speech signal features, applied to SVM (Support Vector Machines) and GMM (Gaussian Mixture Models) classifiers. For continuous speech, the GMM system reaches 74% accuracy rate while the SVM system obtains 72% accuracy rate. For the sustained vowel /a/, the accuracy achieved by the GMM and the SVM is 66% and 69% respectively, a lower result than with continuous speech.
Chapter PDF
Similar content being viewed by others
Keywords
References
Lieberman, P.: Some acoustic measures of the fundamental periodicity of normal and pathologic larynges. J. Acoust. Soc. Amer. 35, 344–353 (1963)
Iwata, S.: Periodicities of pitch perturbations in normal and pathological larynges. J. Acoust. Soc. Amer. 45, 344–353 (1972)
Shama, K., Krishna, A., Niranjan Cholayya, N.U.: Study of harmonics-to-noise ratio and critical-band energy spectrum of speech as acoustic indicators of laryngeal and voice pathology. EURASIP Journal on Advances in Signal Processing 1 (2007)
Cordeiro, H., Fonseca, J., Meneses C.: Spectral Envelope and Periodic Component in Classification Trees for Pathological Voice Diagnostic. In: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4607–4610 (2014)
Dibazar, A., Narayanan S.: A system for automatic detection of pathological speech. In: 36th Asilomar Conf., Signal, Systems & Computers (2002)
Fonseca, E.S., Guido, R.C., Scalassara, P.R., Maciel, C.D., Pereira, J.C.: Wavelet time–frequency analysis and least squares support vector machines for the identification of voice disorders. Comput. Biol. Med. 37, 571–578 (2006)
Sáenz-Lechón, N., Godino-Llorente, J.I., Osma-Ruiz, V., Gómez-Vilda, P.: Methodological issues in the development of automatic systems for voice pathology detection. Biomedical Signal Processing and Control 1, 120–128 (2006)
Scalassara, P.R., Dajer, M.E., Maciel, C.D., Guido, R.C., Pereira, J.C.: Relative entropy measures applied to healthy and pathological voice characterization. Applied Mathematics and Computation 207, 95–108 (2009)
Markaki M., Stylianou Y.: Using modulation spectra for voice pathology detection and classification. In: Proc. IEEE EMBC 2009, Minneapolis, pp. 2514–2517 (2009)
Key Elemetrics, Elemetrics Disordered Voice Database (1994)
Markaki, M., Stylianou, Y.: Voice Pathology Detection and Discrimination Based on Modulation Spectral Features. IEEE Transactions on Audio, Speech, and Language Processing 19, 1938–1948 (2011)
Muhammad, G., Alsulaiman, M., Mahmood, A., Ali, Z.: Automatic voice disorder classification using vowel formants. In: IEEE Int. Conf. Multimedia and Expo (ICME) (2011)
Fonseca, E.S., Pereira, J.C.: Normal versus pathological voice signals. IEEE Engineering in Medicine and Biology Magazine 28, 44–48 (2009)
Carvalho, R.T.S., Cavalcante, C.C., Cortez, P.C.: Wavelet transform and artificial neural networks applied to voice disorders identification. In: Third World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 371–376 (2011)
Cordeiro, H., Fonseca, J., Meneses, C.: Edema and Nodules Identification in vowels using spectral features and jitter. In: CETC 2013, Conference on Electronics, Telecommunications and Computers, Procedia Technology, vol. 17, pp. 202–208 (2014)
Lamel, L., Rabiner, L., Rosenberg, A., Wilpon, J.: An Improved Endpoint Detector for Isolated Word Recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing 29, 777–785 (1981)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI 14 (1995)
Chih-Wei, H., Chih-Jen, L.: A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 13, 415–425 (2002)
Reynolds, D.: Speaker identification and verification using Gaussian mixture speaker models. Speech Communications 17, 91–108 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 IFIP International Federation for Information Processing
About this paper
Cite this paper
Cordeiro, H., Meneses, C., Fonseca, J. (2015). Continuous Speech Classification Systems for Voice Pathologies Identification. In: Camarinha-Matos, L., Baldissera, T., Di Orio, G., Marques, F. (eds) Technological Innovation for Cloud-Based Engineering Systems. DoCEIS 2015. IFIP Advances in Information and Communication Technology, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-319-16766-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-16766-4_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16765-7
Online ISBN: 978-3-319-16766-4
eBook Packages: Computer ScienceComputer Science (R0)