Dimensionality reduction for visualization of normal and pathological speech data

Biomedical Signal Processing and Control

Volumn 4, Issue 3, 2009, Pages 194-201

  Goddard, J ^a   Schlotthauer, G ^b   Torres, M E ^b,c   Rufiner, H L ^b,c  

^a DEPARTAMENTO DE QUÍMICA   (Mexico)

^b UNIVERSIDAD NACIONAL DE ENTRE RÍOS   (Argentina)

^c UNIVERSIDAD NACIONAL DEL LITORAL   (Argentina)

Author keywords

Data visualization; Dimensionality reduction; Kernel methods; Pathological voice analysis

Indexed keywords

BIOMEDICAL SIGNAL PROCESSING; DATA REDUCTION; DATA VISUALIZATION; DISCRIMINANT ANALYSIS; FISHER INFORMATION MATRIX; VISUALIZATION;

DIMENSIONALITY REDUCTION TECHNIQUES; EXPLORATORY DATA ANALYSIS; HIGH-DIMENSIONAL DATASET; KERNEL METHODS; LOCAL FISHER DISCRIMINANT ANALYSIS; PATHOLOGICAL SPEECH SIGNALS; PATHOLOGICAL VOICE; QUALITATIVE AND QUANTITATIVE ANALYSIS;

DIMENSIONALITY REDUCTION;

ARTICLE; CLASSIFIER; CONTROLLED STUDY; DISCRIMINANT ANALYSIS; INFORMATION PROCESSING; KERNEL METHOD; PRIORITY JOURNAL; QUALITATIVE ANALYSIS; QUANTITATIVE ANALYSIS; SPEECH DISORDER; STATISTICAL ANALYSIS;

EID: 67650267749     PISSN: 17468094     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.bspc.2009.01.001     Document Type: Article

References (24)

1. Baken R.J., and Orlikoff R.F. Clinical Measurement of Speech and Voice. 2nd ed. (2000), Singular Thompson Learning
2. Schlotthauer G., Torres M.E., and Jackson-Menaldi M.C. Automatic classification of dysphonic voices. WSEAS Trans. Signal Process. 2 9 (2006) 1260-1267
3. G. Schlotthauer, M.E. Torres, M.C. Jackson-Menaldi, A pattern recognition approach to spasmodic dysphonia and muscle tension dysphonia automatic classification, J. Voice, in press.
4. Sáenz-Lechón N., Godino-Llorente J.I., Osma-Ruiz V., and Gómez-Vilda P. Methodological issues in the development of automatic systems for voice pathology detection. Biomed. Signal Process. Control 1 (2006) 120-128
5. Torres M.E., Gamero L.G., Rufiner H.L., Martínez C., Milone D.H., and Schlotthauer G. Study of complexity in normal and pathological speech signals. Proc. of the 25th Annu. Int. Conf. of the IEEE Eng. in Med. and Biol. Soc. (2003) 2339-2342
6. Jolliffe I.T. Principal Component Analysis, volume XXIX of Springer Series in Statistics. 2nd ed. (2002), Springer
7. Sammon J.W. A Non-Linear Mapping for Data Structure Analysis. IEEE Trans. Comput. C-18(5) (1969) 401-409
8. M.A. Carreira-Perpinan, Continuous latent variable models for dimensionality reduction and sequential data reconstruction, PhD thesis, University of Sheffield, UK, 2001.
9. Jain V., and Saul L.K. Exploratory analysis and visualization of speech and music by locally linear embedding. Proc. Int. Conf. Acoust. Speech Signal Process, vol. 3. Canada (2004) 984-987
10. Kocsor A., and Tóth L. Kernel-based feature extraction with a speech technology application. IEEE Trans. Signal Process. 52 8 (2004) 2250-2263
11. Schölkopf B., Smola A., and Müller K.-R. Kernel Principal Component Analysis, Chapter Advances in Kernel Methods-Support Vector Learning (1998), MIT Press, Cambridge, MA 327-352
12. Lawrence N.D. Probabilistic non-linear principal component analysis with Gaussian process latent variable models. J. Mach. Learn. Res. 6 (2005) 1783-1816
13. Roweis S., and Saul L. Nonlinear dimensionality reduction by locally linear embedding. Science 290 5500 (2000) 2323-2326
14. Shawe-Taylor J., and Cristianini N. Kernel Methods for Pattern Analysis (2004), Cambridge U.P.
15. Sugiyama M. Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J. Mach. Learn. Res. 8 (2007) 1027-1061
16. J. Goddard, F. Martínez, G. Schlotthauer, M.E. Torres, H.L. Rufiner, Visualization of normal and pathological speech data. In: C. Manfredi (Ed.), Proc. of Models and Anal. of Vocal Emissions for Biomed. Appl.: 5th Int. Workshop, Italy, pp. 32-36, 2007.
17. Massachusetts Eye, Voice Ear Infirmary, and Speech Lab. Disorder voice database version 1.03, 1994.
18. Kay Elemetrics Corporation. Disordered Voice Database Model 4337 (1994), MEEIVS Lab. Boston, MA
19. Gelzinis A., Verikas A., and Bacauskiene M. Automated speech analysis applied to laryngeal disease categorization. Comput. Methods Prog. Biomed. 91 (2008) 36-47
20. Boersma P. Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. Institute of Phonetic Sciences, University of Amsterdam, Proc., vol. 17 (1993) 97-110
21. Lawrence N.D. Gaussian process models for visualisation of high dimensional data. Advances in Neural Information Processing Systems (NIPS) (2004), MIT Press, Cambridge, MA
22. Ham J., Lee D.D., Mika S., and Schölkopf B. A kernel view of the dimensionality reduction of manifolds. In: Greiner R., and Schuurmans D. (Eds). Proc. of the 21st. Int. Conf. on Mach. Learn. (2004) 369-376
23. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification. Stat. for Eng. and Inf. Sci. 2nd ed. (2001), Wiley Interscience
24. Kruskal W.H., and Wallis W.A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47 (1952) 583-621