Parkinson's Syndrome Diagnosis Applying Perceptual Linear Prediction Cepstral Analysis on Several Speech Recordings

Parkinson's Syndrome Diagnosis Applying Perceptual Linear Prediction Cepstral Analysis on Several Speech Recordings
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-9
Year of Publication : 2022
Authors : Sara Sandabad, Achraf Benba, Hasna Nhaila
DOI : 10.14445/22315381/IJETT-V70I9P222

How to Cite?

Sara Sandabad, Achraf Benba, Hasna Nhaila, "Parkinson's Syndrome Diagnosis Applying Perceptual Linear Prediction Cepstral Analysis on Several Speech Recordings" International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 214-221, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I9P222

Abstract
This study's objective is to distinguish between two populations: 20 patients with Parkinson's disease (PD) and 20 healthy volunteers. Each person's three sustained vowel types (/a/, /o/, and /u/) were recorded, and then cepstral analysis was performed on these voice signals. In this study, we will use Perceptual Linear Prediction (PLP) to generate voiceprint from every voice sample (PLP). In order to collect the voiceprint from every voice recording, the obtained PLP cepstral coefficients were compressed by calculating their average value. Furthermore, a classification method used in our work is obtained by combining the Support Vector Machines classifier and a com the Leave One Subject Out validation scheme (LOSO). We did an independent test to evaluate our results using another database with 28 PD. According to the research result, combining the linear kernel of SVM and LOSO on the sustained vowel /o/, the best classification accuracy on the first dataset was 80%. And adopting the hybridization of two sustained vowels, /a/ and /o/, with the MLP kernel of the SVM, the maximum classification accuracy using the independent test was 87.50 percent.

Keywords
Voice analysis, Parkinson's disease, Perceptual Linear Prediction, Voiceprint, Leave One Subject Out, Support Vector Machine.

Reference
[1] Little, And Al, "Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's Disease,” Biomedical Engineering, IEEE Transactions on , vol.56, no.4, pp.1015-1022, 2009.
[2] L. Ishihara, And C. Brayne, “A Systematic Review of Depression and Mental Illness Preceding Parkinson's Disease,” Acta Neurologica Scandinavica , vol.113, no.4, pp.211-220, 2009.
[3] J. Jankovic, “Parkinson's Disease: Clinical Features And Diagnosis,” Journal of Neurology, Neurosurgery & Psychiatry, vol.79, no.4 , pp. 368-376, 2008. [4] S. B. O'sullivan, T. J. Schmitz, “Parkinson Disease,” Physical Rehabilitation, 5th Ed. Philadelphia, Pa, Usa: F. A. Davis Company,
pp. 856–894, 2007.
[5] Huse, Daniel M., Et Al, "Burden of Illness in Parkinson's Disease,” Movement Disorders , vol.20, no.11, pp.1449-1454, 2005.
[6] S. Betulerdogdu, Et Al, “Collection and Analysis of a Parkinson Speech Dataset With Multiple Types of Sound Recordings,” Biomedical And Health Informatics, IEEE Journal of , vol.17, no.4, pp.828-834, 2013.
[7] U. K. Rani, M.S. Holi, "Automatic Detection of Neurological Disordered Voices Using Mel Cepstral Coefficients and Neural Networks," 2013 IEEE Point-of-Care Healthcare Technologies (PHT), Bangalore, India, 2013.
[8] M. A. Little, P. E. Mcsharry, S. J. Roberts, D. A. Costello, I. M. Moroz, “Exploiting Non-Linear Recurrence and Fractal Scaling Properties for Voice Disorder Detection,” Biomedical Engineering Online, 2007.
[9] D. A. Rahn, M. Chou, J. J. Jiang, Y. Zhang, “Phonatory Impairment In Parkinson's Disease: Evidence from Non-Linear Dynamic Analysis And Perturbation Analysis,” Journal of Voice. vol.21, pp.64-71, 2007.
[10] H. Hynek, “Perceptual Linear Predictive (PLP) Analysis of Speech,” The Journal of the Acoustical Society of America , vol.87, no.4 , pp.1738-1752, 1990.
[11] Ch. S. Kumar, P. R. Mallikarjuna, "Design of an Automatic Speaker Recognition System Using Mfcc, Vector Quantization And Lbg Algorithm," International Journal on Computer Science And Engineering, vol.3, no.8, 2011.
[12] S. Young, G. Evermann, T. Hain, D. Kershaw, X. Liu, G. Moore, J. Odell, D. Ollason, D. Povey, V. Valtchev, P. Woodland, "The Htk Book (For Htk Version 3.4)," Cambridge University Engineering Department, Copyright. 2001-2006.
[13] J. Makhoul, And L. Cosell, “Lpcw: An Lpc Vocoder With Linear Predictive Spectral Warping,” Acoustics, Speech, And Signal Processing, IEEE International Conference on Icassp'76, IEEE vol. 1, 1976.
[14] J. Makhoul, "Spectral Linear Prediction: Properties and Applications," Acoustics, Speech And Signal Processing, Ieee Transactions on , vol.23, no.3, pp. 283-296, 1975.
[15] Ayush Jain, Prathamesh Patil, Ganesh Masud, Prof. Sunantha Krishnan, Prof. Vijaya Bharathi Jagan, “Detection of Sarcasm Through Tone Analysis on Video and Audio Files: A Comparative Study on Ai Models Performance," SSRG International Journal of Computer Science And Engineering, vol. 8, no. 12, pp. 1-5, 2021. Crossref, https://doi.org/10.14445/23488387/Ijcse-V8i12p101
[16] A. Benba, A. Jilbab And A. Hammouch, "Voice Analysis for Detecting Persons with Parkinson's Disease Using MFCC and VQ,” The 2014 International Conference on Circuits, Systems and Signal Processing, Saint Petersburg State Polytechnic University, Saint Petersburg, Russia, September 23-25, 2014.
[17] A. Benba, A. Jilbab And A. Hammouch, "Hybridization of Best Acoustic Cues For Detecting Persons With Parkinson's Disease," 2nd World Conference on Complex System (Wccs'14), IEEE, Agadir November 10-12, 2014.
[18] A. Benba, A. Jilbab And A. Hammouch, “Voiceprint Analysis Using Perceptual Linear Prediction And Support Vector Machines For Detecting Persons With Parkinson's Disease,” The 3rd International Conference on Health Science And Biomedical Systems (Hsbs '14) Florence, Italy, November 22-24, 2014.
[19] A. Benba, A. Jilbab, And A. Hammouch, "Voice Analysis for Detecting Persons With Parkinson's Disease Using PLP and VQ,” Journal of Theoretical and Applied Information Technology , vol.70, no.3, 2014.
[20] A. Benba, A. Jilbab, And A. Hammouch, “Detecting Patients With Parkinson's Disease Using PLP and VQ,” The 7th International Conference on Information Technology, 12-15 Mai, Amman, Jordan, 2015.
[21] A. Benba, A. Jilbab, And A. Hammouch, “Detecting Patients With Parkinson's Disease Using Mel Frequency Cepstral Coefficients And Support Vector Machines,” International Journal on Electrical Engineering and Informatics , vol.2, no.7, pp. 297 – 307, 2015.
[22] S. Malathi, D. Kannan, "Prediction of Autism Spectrum Disorder Using Reliable Ant Colony Optimisation Based Relevant Vector Machine," International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 57-63, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I9P206.
[23] J. Martinez, H. Perez, E. Escamilla, M. M. Suzuki, "Speaker Recognition Using Mel Frequency Cepstral Coefficients (MFCC) And Vactor Quantization (VQ) Techniques," IEEE Electrical Communications and Computers, Cholula, Puebla, pp. 248-251, 2012.