New computer aided device for real time analysis of speech of people with Parkinson’s disease
Keywords:Parkinson’s disease, portable device, real time speech assessment, vocal space area, jitter, shimmer, pitch, nonlinear dynamics
Parkinson’s disease (PD) is a neurodegenerative disorder that affects the coordination of muscles and limbs, including those responsible of the speech production. The lack of control of the limbs and muscles involved in the speech production process can generate intelligibility problems and this situation has a negative impact in the social interaction of the patients. It is already demonstrated that constant speech therapy can improve the communication abilities of the patients; however, the measurement of the recovery progress is done subjectively by speech therapists and neurologists. Due to this, it is required the development of flexible tools able to asses and guide the speech therapy of the patients. In this paper the design and deployment of a new device for the real time assessment of speech signals of people with PD is presented. The processes of design and deployment include the development on three platforms: first, a graphic user interface is developed on Matlab, second the first prototype is implemented on a digital signal processor (DSP) and third, the final device is developed on a mini-computer. The device is equipped with an audio codec, storage capacity and the processing unit. Besides, the system is complemented with a monitor to display the processed information on real time and with a keyboard enabling the interaction of the end-user with the device.
M. de Rijk, L. Launer, K. Berger, M. Breteler, J. Dartigues, M. Baldereschi, L. Fratiglioni, A. Lobo, J. Martinez, C. Trenkwalder, A. Hofman. “Prevalence of parkinson’s disease in europe: a collaborative study of population-based cohorts”. Journal of Speech Language and Hearing Research. Vol. 54. 2007. pp. 21-23.
F. Martínez. “Trastornos del habla y la voz en la enfermedad de Parkinson”. Neurología. Vol. 51. 2010. pp. 542-550.
S. Skodda, W. Visser, U. Schlegel. “Vowel articulation in Parkinson’s diease”. Journal of Voice. Vol. 25. 2011. pp. 467-472.
J. Mekyska, I. Rektorova, Z. Smekal. Selection of Optimal Parameters for Automatic Analysis of Speech Disorders in Parkinson’s Disease. Proceedings of the 34th International Conference on Telecommunications and Signal Processing (TSP). Budapest, Hungría. 2011. pp. 408-412.
O. Geman. Data Processing for Parkinson’s Disease: Tremor, Speech and Gait Signal Analysis. Proceedings of the 3rd International Conference on E-Health and Bioengineering (EHB). Iaşi, Romania. 2011. pp. 1-4.
A. Tsanas, M. Little, P. McSharry, L. Ramig. “Accurate Telemonitoring of Parkinson’s Disease Progression by Noninvasive Speech Tests”. IEEE transactions on Biomedical Engineering. Vol. 57. 2010. pp. 884-893.
J. Orozco, J. Arias, J. Vargas, E. Nöth. “Perceptual Analysis of Speech Signals from People with Parkinson’s Disease”. J. Ferrández, J. Álvarez, F. de la Paz, F. Toledo (editors). Natural and Artificial Models in Computation and Biology. Lecture Notes in Computer Science. Vol. 7930. Ed. Springer Verlag. Mallorca, Spain. 2013. pp. 201-211.
E. Belalcazar, J. Orozco, J. Vargas, J. Arias, E. Noth. “New Cues in Low-Frequency of Speech for Automatic Detection of Parkinson’s Disease”. J. Ferrández, J. Álvarez, F. de la Paz, F. Toledo (editors). Natural and Artificial Models in Computation and Biology. Lecture Notes in Computer Science. Vol. 7930 . Ed. Springer Verlag. Mallorca, Spain. 2013. pp. 283-292.
J. Orozco, J. Arias, J. Vargas, E. Noth, “Analysis of Speech from People with Parkinson’s Disease through Nonlinear Dynamics”. T. Drugman, T. Dutoit (editors). Advances in Nonlinear Speech Processing. Lecture Notes in Computer Science. Vol. 7911 . Ed. Springer Verlag. Mons, Belgium. 2013. pp. 112-119.
J. Zicker, W. Tompkins, R. Rubow, J. Abbs. “A Portable Microprocessor-Based Biofeedback Training Device”. IEEE transactions on Biomedical Engineering. Vol. 27. 1980. pp. 509-515.
R. Rubow, E. Swift. “A microcomputer-based wearable biofeedback device to improve transfer of treatment in parkinsonian dysarthria”. Journal of Speech and Hearing Disorders. Vol. 50. 1985. pp. 178-185.
L. Ochs. Self adjusting bio-feedback method and apparatus. US Patent 4.461.301, 24 Jul. 1984 Washington, USA. 1984. pp.1-10
J. Dembowski, B. Watson. “An instrumented method for assessment and remediation of stuttering: A singlesubject case study”. Journal in fluency disorder. Vol. 16. 1991. pp. 241-273.
A. S. AlMejrad. “Design of an intelligent system for speech monitoring and treatment of low and excessive vocal intensity”. Artif Life Robotics. Vol. 15. 2010. pp. 320-324.
M. Wirebrand. Real-time monitoring of voice characteristics using accelerometer and microphone measurements. Master Thesis, Linköping University. Linköping, Sweden. 2011. pp.1-46.
A. Carullo, A. Vallan. “Design Issues for a Portable Vocal Analyzer”. IEEE Transactions on instrumentation and measurement. Vol. 62. 2013. pp. 1084-1093.
B. Resch, M. Nilsson, A. Ekman, W. Kleijn. “Estimation of the Instantaneous Pitch of peech”. IEEE Transactions on Audio, Speech, and Language Processing. Vol. 15. 2007. pp. 813-822.
C. Shahnaz, W. Zhu. A new Technique for the Estimation of Jitter and Shimmer of Voiced Speech Signal. Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). Ottawa, Canada. 2006. pp. 2112-2115.
S. Sapir, L. Ramig, J. Spielman, C. Fox. “Formant Centralization Ratio (FCR): A proposal for a new acoustic measure of dysarthric speech”. Journal of Speech, Language and Hearing Research. Vol. 53. 2010. pp. 114-133.
P. Boersma, D. Weenink. Praat: doing phonetics by computer. Available on: http://www.fon.hum.uva.nl. Accessed: Sept. 18, 2013.
P. Boersma. “Accurate Short-Term Analysis of the Fundamental Frequency and the Harmonics-to-Noise Ratio of a Sampled Sound”. Institute of Phonetic Sciences, U. of Amsterdam. Vol. 17. 1993. pp. 97-110.
M. Kammoun, D. Gargouri, M. Frikha, A. Hamida. Linear Prediction Method Evaluation in Speech Formant Frequencies Estimation. Proceedings of the Third International Conference on Systems, Signals & Devices. Sousse, Tunisia. 2005. pp. 1-5.
R. Kent, Y. Kim. “Toward an acoustic typology of motor speech disorders”. Clinical Linguistics & Phonetics. Vol. 17. 2003. pp. 427-445.
H. Herzel, D. Berry, I. Titze. “Analysis of Vocal Disorders With Methods From Nonlinear Dynamics”. Journal of Speech and Hearing Research. Vol. 37. 1994. pp. 1008-1019.
F. Takens. “On the numerical determination of the dimension of an attractor”. Lecture Notes in Mathematics. Vol. 1125. 1985. pp. 99-106.
P. Grassberger and I. Procaccia. “Characterization of Strange Attractors”. Physical review. Vol. 50. 1983. pp. 346–349.
J. Jiang, Z. Yu, M. Clancy. “Chaos in voice, from modeling to measurement”. Journal of Voice. Vol. 20. 2006. pp. 2-17.
V. Oseledec. “A multiplicative ergodic theorem. Lyapunov characteristic numbers for dynamical systems”. Transactional. Moscow Mathematical. Society. Vol. 19. 1968. pp. 197-231.
R. Chassaing, D. Reay. Digital Signal Processing and Applications with TMS320C6713 and TMS320C6416 DSK. 2nd ed. Ed. Wiley Interscience. New Jersey, USA. 2008. pp. 283-290.
Hardkernel, Hardkernel ODROID. Available on: www.hardkernel.com. Accessed: Sept. 15, 2013.
H. Langtangen. Python Scripting for Computational Science. 3rd ed. Ed. Springer Publishing Company. Berlin, Germany. 2009. pp. 1-70.
R. Johansson. Lectures on scientific computing with Python. Available on: https://github.com/jrjohansson/scientific-python-lectures. Accessed: Sept. 15, 2013.
docs, Python. Multiprocessing Process-based threading interface. 2013 Available on: https://docs.python.org/2/library/multiprocessing.html. Accessed: Jul. 21, 2013.
J. Glover, V. Lazzarini, J. Timoney. Python for audio signal processing. Proceedings of the Linux Audio Conference. Maynooth, Ireland. 2011. pp 1-8.
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