Analysis of Speech Algorithms in Disease affected Voice Patterns

Authors

  • Karunanithi. D Information Technology, Hindustan University,Chennai, India
  • Paul Rodrigues Velammal College of Engineering,Chennai, India

Keywords:

Speech Patterns, Speech Algorithms, Speech Analysis, Speech Diagnosis

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.

References

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.

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.

D. Talkin, W. B. Klejin, and K. K. Paliwal, “A Robust Algorithm for Pitch Tracking”, Speech coding and synthesis, Elsevier, New York, 1995.

S. Young, D. Kershaw, J. Odell, D. Ollason V. Valtchev, P.Woodland, “The HTK book”, Microsoft Corporation, July 2000.

“Disorder Database Model 4337” Massachusetts Eye and Ear Infirmary Voice and Speech Lab, Boston, MA, Jan. 2002.

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.

National Collaborating Centre for Chronic Conditions, Parkinson’s Disease, London, U.K.: Royal College of Physicians, 2006.

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.

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.

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.

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.

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.

A. Tsanas, M. A. Little, P. E. McSharry, J. Spielman, and L. O. Ramig,“Novel speech signal processing algorithms for high-accuracy classification of Parkinson’s disease,” IEEE Trans. Biomed. Eng., vol. 59, no. 5, pp. 1264–1271, May 2012.

A. Tsanas, M. A. Little, P. E. McSharry, and L. O. Ramige,“Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity,” J. Royal Society Interface, vol. 8, pp. 842–855, 2011.

A Vibroacoustic Model of Selected Human Larynx Diseases, International Journal of Occupational Safety and Ergonomics (JOSE) , Vol. 13, No. 4, 367–379.

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Published

2014-02-28

How to Cite

[1]
Karunanithi. D and P. Rodrigues, “Analysis of Speech Algorithms in Disease affected Voice Patterns”, Int. J. Comp. Sci. Eng., vol. 2, no. 2, pp. 16–20, Feb. 2014.

Issue

Vol. 2 No. 2 (2014): IJCSE February Edition

Section

Research Article

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