The Separation of Multi-Class Pathological Speech Signals Related to Vocal Cords Disorders Using Adaptation Wavelet Transform Based on Lifting Scheme

Year 2015, Volume: 36 Issue: 3, 2371 - 2382, 13.05.2015

Porya Salehı

Abstract

Abstract. Achievement to a non-invasive method to properly diagnose the diseases is a significant subject in domain of speech processing. The aim of this paper is to apply non-invasive methods to do diagnosis, provide preventive strategies and plan for treatment aids and treatment. Regarding the speech disorders based on wavelet features of common wavelet although have relatively proper performance, it is expected that design optimizations based on the features of speech signal and classifier performance lead to improvement of results. To design the adaptive wavelet transform, the parameters of lifting scheme generating bi-orthogonal wavelet are initially applied and then they are optimized through genetic algorithm and classification performance of Support Vector Machine. The result separation of normal and pathological signals provides an accuracy of 100 percent. Also, the result of two-class and three-class separation of six disorders using adaptation wavelet based on lifting scheme which indicative the advantage of suggested method with other mother wavelet.

Keywords

Lifting Scheme, Speech Signal Processing, Vocal Cords Disorders, Support Vector Machine, Genetic Algorithm

References

• J. A. Londono, J. Godino, N. S. Lechon, V. O. Ruiz, G. C. Domınguez, (2011),“Automatic Detection of Pathological Voices Using Complexity Measures, Noise Parameters, and Mel- Cepstral Coefficients,” IEEE Transactions on Biomedical Engineering, 58 (2), pp. 370-379.
• H. Cordeiro, H. Fonseca, C. M. Ribeiro, (2013),“LPC Spectrum First Peak Analysis for Voice Pathology Detection,” International Conference on Health and Social Care Information Systems and Technologies (HCIST), 9 (1), pp. 1104-1111.
• M. Fezari, F. Amara, I. M. El-Emary, (2014),“Acoustic Analysis for Detection of Voice Disorders Using Adaptive Features and Classifiers,” International Conference on Circuits, Systems and Control, ISBN: 978-1-61804-216-3, pp. 112-117.
• R. Behroozmand, F. Almasganj, (2005),“Comparison of Neural Network and Support Vector Machines Applied to Optimized Features Extracted from Patients’ Speech Signal for Classification of Vocal fold Inflammation,” IEEE International Symposium on Signal Processing and Information Technology, pp. 844-849.
• P. Tataranian Hosseini, F. Almasganj, M. R. Darabad, (2008),“Pathological Voice Classifcation Using Local Discriminant Basis and Genetic Algorithm,” 16th Mediterranean Conference on Control and Automation Congress Centre, France, pp. 872-876.
• R. T. Santos Carvalho, C. Cavalcante, P. C. Cortez, (2011),“Wavelet Transform and Artificial Neural Networks Applied to Voice Disorders Identification,” Third World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 371-376.
• A. Akbari, M. Khalil Arjmandi, (2014),“An Efficient Voice Pathology Classification Scheme Based on Applying Multi-Layer Linear Discriminant Analysis to Wavelet Packet- Based Features,” Biomedical Signal Processing and Control, 10, pp. 209–223.
• Disordered Voice Database, Version 1.03, (1994), Massachusetts Eye and Ear Infirmary, Voice and Speech Lab, Boston MA, Kay Elemetrics Corporation.
• I. Daubechies, W. Sweldens, (1998),“Factoring Wavelet Transforms into Lifting Steps, The Journal of Fourier analysis and Applications, 4 (3), pp 247-269.
• J. Neumann, C. Schnorr, G. Steid, (2005),“Efficientwavelet Adaptation for Hybridwavelet– large Margin Classifiers,” Pattern Recognition Society, 38, pp. 1815-1830.
• N. Yang, R. Muraleedharan, J. Kohl, I. Demirkol, W. Heinzelman and M. Sturge-Apple, (2012), “Speech-Based Emotion Classification Using Multiclass SVM with Hybrid Kernel and Thresholding Fusion,” IEEE Spoken Language Technology Workshop (SLT), pp. 415- 460.
• J. Wang, C. Jo, (2007),“Vocal Folds Disorder Detection using Pattern Recognition Methods,” 29th Annual International Conference of the IEEE, pp.3253-3256.
• R. L. Claypoole, R. G. Baraniuk, R. D. Nowak, (1998),“Adaptive Wavelet Transforms via Lifting, Proceeding of Acoustic,” Speech and Signal Processing, 3 (1) , pp. 1513-1516.
• S. Samanta, (2014),“Genetic Algorithm: An Approach for Optimization (Using MATLAB),” International Journal of Latest Trends in Engineering and Technology (IJLTET), 1(3), pp. 261-267.
• N. Erfanian Saeedi, F. Almasganj, F. Torabinejad, (2011),“Support vector wavelet adaptation for pathological voice assessment,” Computers in Biology and Medicine, 41, pp. 822–828.
• J. Nayak, P.S. Bhat, R. Acharya, U.V. Aithal, (2005),“Classification and analysis of speech abnormalities,” ITBM-RBM 26(5-6), pp. 319–326.
• E. Vaiciukynas, A. Verikas, A. Gelzinis, M. Bacauskiene, V. Uloza, (2012),“Exploring similarity-based classification of larynx disorders from human voice,” Speech Communication 54 (5), pp. 601-610.
• H. KhadiviHeris, B. SeyedAghazadeh, M. Nikkhah-Bahrami, (2012),“Optimal feature selection for the assessment of vocal fold disorders,” Computer Biol. Med. 39 (10), pp. 860–868.