Analysis of Speech Algorithms in Disease affected Voice Patterns
D. Karunanithi1 , P. Rodrigues2
Section:Research Paper, Product Type: Journal Paper
Volume-2 ,
Issue-2 , Page no. 16-20, Feb-2014
Online published on Feb 28, 2014
Copyright © D. Karunanithi, P. Rodrigues . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
View this paper at Google Scholar | DPI Digital Library
How to Cite this Paper
- IEEE Citation
- MLA Citation
- APA Citation
- BibTex Citation
- RIS Citation
IEEE Style Citation: D. Karunanithi, P. Rodrigues, “Analysis of Speech Algorithms in Disease affected Voice Patterns,” International Journal of Computer Sciences and Engineering, Vol.2, Issue.2, pp.16-20, 2014.
MLA Style Citation: D. Karunanithi, P. Rodrigues "Analysis of Speech Algorithms in Disease affected Voice Patterns." International Journal of Computer Sciences and Engineering 2.2 (2014): 16-20.
APA Style Citation: D. Karunanithi, P. Rodrigues, (2014). Analysis of Speech Algorithms in Disease affected Voice Patterns. International Journal of Computer Sciences and Engineering, 2(2), 16-20.
BibTex Style Citation:
@article{Karunanithi_2014,
author = {D. Karunanithi, P. Rodrigues},
title = {Analysis of Speech Algorithms in Disease affected Voice Patterns},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2014},
volume = {2},
Issue = {2},
month = {2},
year = {2014},
issn = {2347-2693},
pages = {16-20},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=45},
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=45
TI - Analysis of Speech Algorithms in Disease affected Voice Patterns
T2 - International Journal of Computer Sciences and Engineering
AU - D. Karunanithi, P. Rodrigues
PY - 2014
DA - 2014/02/28
PB - IJCSE, Indore, INDIA
SP - 16-20
IS - 2
VL - 2
SN - 2347-2693
ER -
VIEWS | XML | |
4180 | 3833 downloads | 3896 downloads |
Abstract
The speech is the most important and effective method in the Human Interaction. Speech signal is the basis of Human Computer Interaction and Human Communications Technology. Many Human Diseases like Parkinson Disease, Cerebellar Demyelization, Stroke and many Neurological Diseases are analyzed using the their speech patterns for diagnosing the disease. To analyze these pathological voice patterns algorithms are used. In this paper, various algorithms used to analyze the voice patterns are analyzed and also comparative study of Parkinson and Larynx Diseases are analyzed using the voice patterns.
Key-Words / Index Term
Speech Patterns; Speech Algorithms; Speech Analysis; Speech Diagnosis
References
[1]. M. D. O. Rosa, J. C. Pereira, M. Grellet, �Adaptive Estimation of Residue Signal for Voice Pathology Diagnosis�, IEEE Trans. Biomedical Eng. Vol. 47, No. 1, Jan. 2000.
[2]. L. G. Ceballos, and H. L. Hansen, �Direct Speech Feature Estimation Using an Iterative EM Algoritm for Vocal Fold Pathology Detection�, IEEE Trans. Biomedical Eng. Vol. 43, No. 4, April. 1996.
[3]. D. Talkin, W. B. Klejin, and K. K. Paliwal, �A Robust Algorithm for Pitch Tracking�, Speech coding and synthesis, Elsevier, New York, 1995.
[4]. S. Young, D. Kershaw, J. Odell, D. Ollason V. Valtchev, P.Woodland, �The HTK book�, Microsoft Corporation, July 2000.
[5]. �Disorder Database Model 4337� Massachusetts Eye and Ear Infirmary Voice and Speech Lab, Boston, MA, Jan. 2002.
[6]. M. A. Little, P. E. McSharry, E. J. Hunter, J. Spielman, and L. O. Ramig,�Suitability of dysphonia measurements for telemonitoring of Parkinson�s disease,� IEEE Trans. Biomed. Eng., vol. 56, no. 4, pp. 1010�1022, Apr.2009.
[7]. National Collaborating Centre for Chronic Conditions, Parkinson�s Disease, London, U.K.: Royal College of Physicians, 2006.
[8]. L. Cunningham, S. Mason, C. Nugent, G. Moore, D. Finlay, and D. Craig, �Home-based monitoring and assessment of Parkinson�s disease,� IEEE Trans. Inf. Technol. Biomed., vol. 15, no. 1, pp. 47�53, Jan. 2011.
[9]. G. Rigas, A. Tzallas, M. Tsipouras, P. Bougia, E. Tripoliti, D. F. D. Baga, S. Tsouli, and S. Konitsiotis, �Assessment of Tremor activity in the Parkinson�s disease using a set of wearable sensors,� IEEE Trans. Inf. Technol.Biomed., vol. 16, no. 3, pp. 478�487, May 2012.
[10]. S. Marino, R. Ciurleo, G. Lorenzo, M. Barresi, S. De Salvo, S. Giacoppo, A. Bramanti, P. Lanzafame, and P. Bramanti, �Magnetic resonance imaging markers for early diagnosis of Parkinson�s disease,� Neural Regeneration Res., vol. 7, no. 8, pp. 611�619, 2012.
[11]. Z. Dastgheib, B. Lithgow, and Z. Moussavi, �Diagnosis of Parkinson�s disease using electrovestibulography,� Med. Biol. Eng. Comput., vol. 50, no. 3, pp. 483�491, 2012.
[12]. M. A. Little, P. E. McSharry, S. J. Roberts, D. A. E. Costello, and I. M. Moroz, �Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection,� Biomed. Eng. Online, vol. 6, no. 23, 2007, doi: 10.1186/1475-925X-6-23.
[13]. A. Tsanas, M. A. Little, P. E. McSharry, J. Spielman, and L. O. Ramig,�Novel speech signal processing algorithms for high-accuracy classiï¬cation of Parkinson�s disease,� IEEE Trans. Biomed. Eng., vol. 59, no. 5, pp. 1264�1271, May 2012.
[14]. A. Tsanas, M. A. Little, P. E. McSharry, and L. O. Ramige,�Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantiï¬cation of average Parkinson�s disease symptom severity,� J. Royal Society Interface, vol. 8, pp. 842�855, 2011.
[15]. A Vibroacoustic Model of Selected Human Larynx Diseases, International Journal of Occupational Safety and Ergonomics (JOSE) , Vol. 13, No. 4, 367�379.