Abstract
The voice pathology detection using automatic classification systems is a useful way to diagnose voice diseases. In this paper, we propose a novel tool to detect voice pathology based on an incremental possibilistic SVM-HMM method which can be applied to serval practical applications using non-stationary or a very large-scale data in purpose to reduce the memory issues faced during the storage of the kernel matrix. The proposed system includes the steps of using SVM to incrementally compute possibilitic probabilities and then they will be used by HMM in order to detect voice pathologies. We evaluated the proposed method on the task of the detection of voice pathologies using voices samples from the Massachusetts Eye and Ear Infirmary Voice and Speech Laboratory (MEEI) database. According to the detection rates obtained by our system, the performance sounds robust, efficient and speed applied to a task of voices pathology detection.
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
References
Cauwenberghs, G., Poggio, T.: Incremental and decremental support vector machine learning. In: Advances in Neural Information Processing Systems (NIPS), vol. 13, pp. 409–415 (2001)
Smets, P.: The combination of evidence in the transferable belief model. IEEE Trans. Pattern Anal. Mach. Intell. 12, 447–458 (1990)
Davis, S.B., Mermelstein, P.: Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans. Acoust. Speech Signal Process. 28, 357–366 (1980)
Amami, R., Ben Ayed, D., Ellouze, N.: Incorporating belief function in SVM for phoneme recognition. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, J.-S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS (LNAI), vol. 8480, pp. 191–199. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07617-1_17
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995). https://doi.org/10.1007/978-1-4757-2440-0
Hofmann, M.: Support Vector Machines-kernels and the Kernel Trick. An elaboration for the Hauptseminar, Reading Club: Support Vector Machines (2006)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)
Alireza Dibazar, A., Narayanan, S., Berger, T.W.: Feature analysis for automatic detection of pathological speech. In: Conference of the Engineering in Medicine and Biology, vol. 1 (2002)
Patil, H.A., Baljekar, P.N.: Classification of normal and pathological voices using TEO phase and Mel cepstral features. In: International Conference on Signal Processing and Communications (SPCOM), pp. 01–05 (2012)
Amami, Rimah, Ben Ayed, Dorra: Robust noisy speech recognition using deep neural support vector machines. In: De La Prieta, Fernando, Omatu, Sigeru, Fernández-Caballero, Antonio (eds.) DCAI 2018. AISC, vol. 800, pp. 300–307. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-94649-8_36
Pend, C., Xu, Q.J., Wan, B.K., Chen, W.X.: Pathological voice classification based on features dimension optimization, Transactions of Tianjin University, vol. 13, no. 6 (2007)
Hariharan, M., Polat, K., Sindhu, R., Yaacob, S.: A hybrid expert system approach for telemonitoring of vocal fold pathology. Appl. Soft Comput. 13, 4148–4161 (2013)
Amami, R., Smiti, A.: An incremental method combining density clustering and Support Vector Machines for voice pathology detection. Comput. Electr. Eng. 57, 257–265 (2016)
Dibazar, A.A., Berger, T.W., Narayanan, S.S.: Pathological voice assessment. In: IEEE 28th Engineering in Medicine and Biology Society, New York, USA, pp. 1669–1673, August 2006
Majidnezhad, V., Kheidorov, I.: A HMM-based method for vocal fold pathology diagnosis. IJCSI Int. J. Comput. Sci. Issues 9(6) (2012). No 2
Maragos, P., Kaiser, J., Quatieri, T.: On separating amplitude from frequency modulations using energy operators. In: Proceedings of International Conference on Acoustics, Speech, and Signal Processing ICASSP, vol. 2, pp. 1–4 (1992)
Rabiner, L.R.: A tutorial on Hidden Markov Models and selected applications in speech recognition (1989)
Amara, F., Fezari, M., Bourouba, H.: An improved GMM-SVM system based on distance metric for voice pathology detection. Appl. Math. Inf. Sci. J. 10(3), 1061–1070 (2016)
Zulfiqar, A., Irraivan, E., Mansour, A., Ghulam, M.: Automatic voice pathology detection with running speech by using estimation of auditory spectrum and cepstral coefficients based on the all-pole model. J. Voice 30(6), 757.e7–757.e19 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Amami, R., Amami, R., Eleraky, H.A. (2019). An Incremental System for Voice Pathology Detection Combining Possibilistic SVM and HMM. In: MartÃn-Vide, C., Purver, M., Pollak, S. (eds) Statistical Language and Speech Processing. SLSP 2019. Lecture Notes in Computer Science(), vol 11816. Springer, Cham. https://doi.org/10.1007/978-3-030-31372-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-31372-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31371-5
Online ISBN: 978-3-030-31372-2
eBook Packages: Computer ScienceComputer Science (R0)