Glottal pathology discrimination using ANN and SVM

Use of modern technological advances in real-time biomedical analysis is very crucial. Current work focuses on glottal pathology discrimination based on non-invasive speech analysis techniques. Primary set back in developing such method is irregular performance depreciation of several state of the art acoustic features. To excuse such problems, we have used glottal to noise excitation ratio, which predicts the breathiness quotient of the speech signal and is supported by characteristic mean pitch value. To build a judicial model, we have used Artificial Neural Network (ANN) and Support Vector Machine (SVM). Categorization performance is compared using well known parameters like true positive rate, true negative rate and accuracy. Results of the analysis show slightly favored performance for SVM based decisive system.

[1]  Antanas Verikas,et al.  Categorizing normal and pathological voices: automated and perceptual categorization. , 2011, Journal of voice : official journal of the Voice Foundation.

[2]  Antanas Verikas,et al.  Automated speech analysis applied to laryngeal disease categorization , 2008, Comput. Methods Programs Biomed..

[3]  Vahid Majidnezhad,et al.  An ANN-based Method for Detecting Vocal Fold Pathology , 2013, ArXiv.

[4]  Adas Gelzinis,et al.  On Feature Extraction for Voice Pathology Detection from Speech Signals , 2011 .

[5]  Hans Werner Strube,et al.  Glottal-to-Noise Excitation Ratio - a New Measure for Describing Pathological Voices , 1997 .

[6]  2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, Kochi, India, August 10-13, 2015 , 2015, ICACCI.

[7]  Pedro Gómez Vilda,et al.  Dimensionality Reduction of a Pathological Voice Quality Assessment System Based on Gaussian Mixture Models and Short-Term Cepstral Parameters , 2006, IEEE Transactions on Biomedical Engineering.

[8]  Pedro Gómez-Vilda,et al.  The effectiveness of the glottal to noise excitation ratio for the screening of voice disorders. , 2010, Journal of voice : official journal of the Voice Foundation.

[9]  V. Sellam,et al.  Classification of Normal and Pathological Voice Using SVM and RBFNN , 2014 .

[10]  G. Daqi,et al.  Support vector machine classifiers using RBF kernels with clustering-based centers and widths , 2007, 2007 International Joint Conference on Neural Networks.

[11]  M.G. Bellanger,et al.  Digital processing of speech signals , 1980, Proceedings of the IEEE.

[12]  Tim Ritchings,et al.  Pathological voice quality assesment using artificial neural networks , 2001, MAVEBA.

[13]  Stefan Hadjitodorov,et al.  A computer system for acoustic analysis of pathological voices and laryngeal diseases screening. , 2002, Medical engineering & physics.