Feature analysis of pathological speech signals using local discriminant bases technique

Medical and Biological Engineering and Computing

Volumn 43, Issue 4, 2005, Pages 457-464

  Umapathy, K ^a   Krishnan, Sridhar ^b  

^a UNIVERSITY OF WESTERN ONTARIO   (Canada)

^b RYERSON UNIVERSITY   (Canada)

Author keywords

Dissimilarity measures; Linear discriminant analysis; Local discriminant bases; Pathological speech signals; Wavelet packets

Indexed keywords

ALGORITHMS; FEATURE EXTRACTION; PATIENT MONITORING; PATTERN RECOGNITION; SPEECH ANALYSIS; SPEECH INTELLIGIBILITY; SPEECH PROCESSING;

DISSIMILARITY MEASURES; LINEAR DISCIMINANT ANALYSIS; LOCAL DISCRIMINANT BASES; PATHOLOGICAL SPEECH SIGNALS; WAVELET PACKETS;

PATHOLOGY;

ACCURACY; ALGORITHM; ARTICLE; CLASSIFICATION; COMPUTER ANALYSIS; DATA BASE; FEMALE; HUMAN; MALE; SIGNAL PROCESSING; SPEECH DISORDER;

ALGORITHMS; DATABASES, FACTUAL; DISCRIMINANT ANALYSIS; FEMALE; HUMANS; MALE; SIGNAL PROCESSING, COMPUTER-ASSISTED; SPEECH ACOUSTICS; SPEECH DISORDERS; SPEECH PRODUCTION MEASUREMENT;

EID: 27744462291     PISSN: 01400118     EISSN: None     Source Type: Journal    
DOI: 10.1007/BF02344726     Document Type: Article

References (36)

1. Agbinya,J. I. (1996): 'Discrete wavelet transform techniques in speech processing'. Proc. IEEE TENCON, Digital Signal Processing Applications, 2, pp. 514-519
2. Athineos, M., and Ellis, D. P. W. (2003): 'Frequency-domain linear prediction for temporal features'. Proc. IEEE Workshop on Automatic Speech Recognition & Understanding, ASRU '03, pp. 261-266
3. Baken, R. J., and Orlikoff, R. F. (2000): 'Clinical measurement of speech and voice' (Singular Publications, SanDiego, CA, 2000)
4. Christian, B. (2002): 'Local discriminant bases and optimized wavelet to classify ultrasonic echoes: application to indoor mobile robotics', Proc. IEEE, Sensors, 2, pp. 1654-1659
5. Coifman R. R., and Wickerhauser, M. V. (1992): 'Entropy-based algorithms for best basis selection', IEEE Trans. Inform. Theory, 38, pp. 713-718
6. Database (1994): 'Voice disorders database, version 1.03 (CDROM)'. Massachusetts Eye & Ear Infirmary, Kay Elemetrics Corporation, Lincoln Park, NJ, USA
7. Eskenazi, L., Childers, D. G., and Hicks, D. M. (1990): 'Acoustics correlates of vocal quality', J. Speech Hearing Res., 33, pp. 298-306
8. Fukunaga, K. (1990): 'Introduction to statistical pattern recognition' (Academic Press, Inc., San Diego, CA, 1990)
9. Godino-Llorente, J. I., and Gomez-Vilda, P. (2004): 'Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors', IEEE Trans. Biomed. Eng., 51, pp. 380-384
10. Hammarberg, B. (1980): 'Perceptual and acoustic correlates of abnormal voice qualities', Acta Otolaryngol. 90, pp. 441-451
11. Hermansky, H., Tsuga, K., Makino, S., and Wakita, H. (1986): Perceptually based processing in automatic speech recognition. In Proc. of the International Conference on Acoustics, Speech, and Signal Processing ICASSP '86. pp. 1971-1974
12. Hillman, R. E., Qui, Y., and Milstein, C. (1999): 'The estimation of signal-to-noise ratio in continuous speech for disordered voices', J. Acoust. Soc. Am., 105, pp. 2532-2535
13. Hsieh, C. T., Lai, E., and Wang, Y. C. (2002): 'Robust speech features based on wavelet transform with application to speaker identification', IEE Proc. Vis. Image Signal Process., 149, pp. 108-114
14. Juang, B. H., and Chen, T. (1998): 'The past, present and future of speech processing', IEEE Signal Process. Mag., 15, pp. 28-48
15. Kadambe, S., and Srinivasan, P. (1994): 'Application of adaptive wavelets for speech coding'. Proc. IEEE-SP Int. Symposium on Time-frequency and Time-scale analysis, pp. 632-635
16. Klingholz, F. (1990): 'Acoustic recognition of voice disorders: A comparative study, running speech versus sustained vowels', J. Acoust. Soc. Am., 87, pp. 2218-2224
17. Krishnan, S., Rangayyan, R. M., Bell, G. D., Frank, C. B., and Ladly, K. O. (1996): 'Screening of knee joint vibroarthrographic signals by statistical pattern analysis of dominant poles'. Proc. Int. Conf. on Engineering in Medicine & Biology - Bridging Disciplines for Biomedicine, pp. 968-969
18. Long, C. J., and Datta, S. (1996): 'Wavelet based feature extraction for phoneme recognition'. Proc. Fourth Int. Conf. on Spoken Language, ICSLP, pp. 264-267
19. Lukasik, E. (2000): 'Wavelet packets based feature selection for voiceless plosives classification'. Proc. Int. Conf. on Acoustics, Speech, & Signal Processing, ICASSP-2000, pp. II689-II692
20. Mallat, S. G., and Zhang, Z. (1993): 'Matching pursuit with time-frequency dictionaries', IEEE Trans. Signal Process., 41, 3397-3415
21. Mallat, S. (1998): 'A wavelet tour of signal processing' (Academic Press, San Diego, CA, 1998)
22. Najih, A. M. M. A., Ramli BIN, A. R., Prakash, V., and Syed, A. R. (2003): 'Speech compression using discrete wavelet transform'. Proc. 4th National Conf. on Telecommunication Technology, pp. 1-4
23. Parsa, V., and Jamieson, D. G. (2000): 'Identification of pathological voices based on glottal noise measures'. J. Speech Hearing Res., 43, pp. 469-485
24. Parsa, V., and Jamieson, D. G. (2001): 'Acoustic discrimination of pathological voice: Sustained vowels versus continuous speech'. J. Speech, Lang. Hearing Res., 44, pp. 327-339
25. Picone, J. W. (1993): 'Signal modelling techniques in speech recognition'. Proc. IEEE Trans., 81, pp. 1215-1247
26. Ris, C., Fontaine, V., and Leich, H. (1995): 'Speech analysis based on Malvar wavelet transform'. Proc. Int. Conf. Acoustics, Speech & Signal Processing ICASSP-95, pp. 389-392
27. Saito, N., & Coifman, R. R., (1995):'Local discriminant bases and their applications'. J. Math. Imag. Vis. 5, pp. 337-358
28. Spooner, C. M. (2001): 'Application of local discriminant bases to HRR-based ATR'. Proc. Signals, Systems & Computers, 35th Asilomar Conf., pp. 1067-1073
29. Spss (1990): SPSS advanced statistics users guide, in 'User manual', (SPSS Inc., Chicago, IL, 1990)
30. Suleesathira, R., and Chaparro, L. F. (1998): 'Evolutionary spectral analysis using Malvar wavelet transform'. Proc. Int. Symposium on Time-frequency & Time-scale analysis, IEEE SP, pp. 673-676
31. Tzanetakis, G., and Cook, P. (2002): 'Musical genre classification of audio signals', IEEE Trans. Speech Audio Process., 10 pp. 293-302
32. Umapathy, K., Krishnan, S., Parsa, V., and Jamieson, D. G. (2002): 'Time-frequency modeling and classification of pathological voices.' Proc. IEEE Engineering in Medicine & Biology Society (EMBS) 2002 Conf., Houston, Texas, USA, pp. 116-117
33. Umapathy, K., and Krishnan, S. (2004): 'Modified local discriminant bases and its applications in signal classification'. Proc. Int. Conf. on Acoustics, Speech & Signal Processing 2004 ICASSP 2004, 2, pp.745-748
34. Veselinovic, D. and Graupe, D. (2003): 'A wavelet transform approach to blind adaptive filtering of speech from unknown noises'. IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process. 50, pp. 150-154
35. Web, A. (2002): Statistical pattern recognition, (Wiley, West Sussex, England, 2002)
36. Yumato, E., Sasaki, Y., and Okamura, H. (1984): 'Harmonics-to-noise ratio and psychophysical measurement of the degree of hoarseness'. J. Speech Hearing Res., 27, pp. 2-6