STATISTICAL ANALYSIS OF THE SIGNAL AND PROSODIC SIGN OF COGNITIVE IMPAIRMENT IN ELDERLY-SPEECH - A Preliminary Study

Shohei Kato, Yuta Suzuki, Akiko Kobayashi, Toshiaki Kojima, Hidenori Itoh, Akira Homma

Abstract

This paper presents a novel approach for early detection of cognitive impairment in the elderly. Our approach incorporates the use of speech sound analysis and multivariate statistical techniques. In this paper, we focus on the prosodic features of speech. Fifty six Japanese subjects (22 males and 34 females between the ages of 64 and 90 years) participated in this study. Blind to clinical groups, we collected speech sounds from segments of dialogue during an HDS-R examination. The segments corresponds to speech sounds from answers to questions about time orientation and number backward counting. Ninety eight prosodic features were extracted from each of the speech sounds. These prosodic features consisted of spectral and pitch features (13), formant features (61), intensity features (22), and speech rate and response time (2). These features were refined by principal component analysis and/or feature selection. In addition, we calculated speech prosody-based cognitive impairment rating (SPCIR) by multiple linear regression analysis. The results indicate that a moderately significant correlation exists between the HDS-R score and the synthesis of several selected prosodic features. Consequently, the adjusted coefficient of determination (R2 = 0.57) suggests that prosody-based speech sound analysis could potentially be used to detect cognitive impairment in elderly subjects.

References

  1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6):716-723.
  2. Awata, S. (2009). Roll of the dementia medical center in the community. In Japanese Journal of Geriatrics, volume 46, pages 203-206. (in Japanese).
  3. Buschke, H., Kuslansky, G., Katz, M., Stewart, W. F., Sliwinski, M. J., Eckholdt, H. M., and Lipton, R. B. (1999). Screening for dementia with the Memory Impairment Screen. Neurology, 52(2):231-238.
  4. Cho, J., Kato, S., and Itoh, H. (2009). Comparison of Sensibilities of Japanese and Koreans in Recognizing Emotions from Speech by using Bayesian Networks. In IEEE International Conference on Systems, Man, and Cybernetics, pages 2945-2950.
  5. Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., and Taylor, J. G. (2001). Emotion recognition in human-computer interaction. IEEE Signal Processing Magazine, 18(1):32-80.
  6. Draper, N. and Smith, H. (1998). Applied Regression Analysis (3rd edition). John Wiley & Sons.
  7. Folstein, M. F., Folstein, S. E., and McHugh, P. R. (1975). “Mini-Mental State”: A practical method for grading the cognitive state of patients for the clinician. J. Psychiat. Res, 12(3):189-198.
  8. Hoyte, K., Brownell, H., and Wingfield, A. (2009). Components of Speech Prosody and their Use in Detection of Syntactic Structure by Older Adults. Experimental Aging Research, 35(1):129-151.
  9. Imai, Y. and Hasegawa, K. (1994). The revised Hasegawa's Dementia Scale (HDS-R): evaluation of its usefulness as a screening test for dementia. J. Hong Kong Coll. Psychiatr., 4(SP2):20-24.
  10. Massy, W. F. (1965). Principal Components Regression in Exploratory Statistical Research. Journal of the American Statistical Association, 60(309):234-256.
  11. Morris, J. C. (1993). The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology, 43(11):2412-2414.
  12. Scherer, K. R., Johnstone, T., and Klasmeyer, G. (2003). Vocal expression of emotion. R. J. Davidson, H. Goldsmith, K. R. Scherer eds., Handbook of the Affective Sciences (pp. 433-456), Oxford University Press.
  13. Schölkopf, B., Smola, A., and Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5):1299-1319.
  14. Schwarz, G. E. (1978). Estimating the Dimension of a Model. The Annals of Statistics, 6(2):461-464.
  15. Taler, V., Baum, S. R., Chertkow, H., and Saumier, D. (2008). Comprehension of grammatical and emotional prosody is impaired in Alzheimer's disease. Neuropsychology, 22(2):188-195.
  16. Taler, V. and Phillips, N. (2007). Language performance in Alzheimer's disease and mild cognitive impairment: A comparative review. Journal of Clinical and Experimental Neuropsychology, 30(5):501-556.
Download


Paper Citation


in Harvard Style

Kato S., Suzuki Y., Kobayashi A., Kojima T., Itoh H. and Homma A. (2011). STATISTICAL ANALYSIS OF THE SIGNAL AND PROSODIC SIGN OF COGNITIVE IMPAIRMENT IN ELDERLY-SPEECH - A Preliminary Study . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011) ISBN 978-989-8425-35-5, pages 322-327. DOI: 10.5220/0003189903220327


in Bibtex Style

@conference{biosignals11,
author={Shohei Kato and Yuta Suzuki and Akiko Kobayashi and Toshiaki Kojima and Hidenori Itoh and Akira Homma},
title={STATISTICAL ANALYSIS OF THE SIGNAL AND PROSODIC SIGN OF COGNITIVE IMPAIRMENT IN ELDERLY-SPEECH - A Preliminary Study},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)},
year={2011},
pages={322-327},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003189903220327},
isbn={978-989-8425-35-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)
TI - STATISTICAL ANALYSIS OF THE SIGNAL AND PROSODIC SIGN OF COGNITIVE IMPAIRMENT IN ELDERLY-SPEECH - A Preliminary Study
SN - 978-989-8425-35-5
AU - Kato S.
AU - Suzuki Y.
AU - Kobayashi A.
AU - Kojima T.
AU - Itoh H.
AU - Homma A.
PY - 2011
SP - 322
EP - 327
DO - 10.5220/0003189903220327