An investigation of manifold learning for speech analysis

INTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP

Volumn 5, Issue , 2006, Pages 2506-2509

  Errity, Andrew ^a   McKenna, John ^a  

^a DUBLIN CITY UNIVERSITY   (Ireland)

Author keywords

Classification; Dimensionality reduction; Manifold learning; Speech analysis

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA MINING; DEGREES OF FREEDOM (MECHANICS); SPEECH; SPEECH ANALYSIS;

DIMENSIONALITY REDUCTION; HIGH DIMENSIONAL SPACES; LINEAR DIMENSIONALITY REDUCTION; LOW DIMENSIONAL STRUCTURE; LOW-DIMENSIONAL SPACES; MANIFOLD LEARNING; MANIFOLD LEARNING ALGORITHM; MANIFOLD STRUCTURES;

LEARNING ALGORITHMS;

EID: 38549178877     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. R. Togneri, M. Alder, and J. Attikiouzel, "Dimension and structure of the speech space," IEE Proceedings-I, vol. 139, no. 2, pp. 123-127, 1992.
2. A. Jansen and P. Niyogi, "A geometric perspective on speech sounds," Tech. Rep., University of Chicago, June 2005.
3. I.T. Jolliffe, Principal Component Analysis, Springer Series in Statistics. Springer-Verlag, New York, 1986.
4. S. T. Roweis and L. K. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol. 290, no. 5500, pp. 2323-2326, December 2000.
5. J. B. Tenenbaum, V. de Silva, and J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science, vol. 290, pp. 2319-2323, 2000.
6. M. Belkin and P. Niyogi, "Laplacian eigenmaps and spectral techniques for embedding and clustering," in Advances in Neural Information Processing Systems. 2002, vol. 14, pp. 585-591, MIT Press.
7. V. Jain and L.K. Saul, "Exploratory analysis and visualization of speech and music by locally linear embedding," in Proc. ICASSP, 2004, vol. 3, pp. 984-987.
8. Rajesh M. Hegde and Hema A. Murthy, "Cluster and intrinsic dimensionality analysis of the modified group delay feature for speaker classification," Lecture Notes in Computer Science, vol. 3316, pp. 1172-1178, January 2004.
9. M. Belkin and P. Niyogi, "Semi-supervised learning on riemannian manifolds," Machine Learning, vol. 56, no. 1 - 3, pp. 209-239, July 2004.
10. L. K. Saul and S. T. Roweis, "Think globally, fit locally: unsupervised learning of low dimensional manifolds," Journal of Machine Learning Research, vol. 4, pp. 119-155, 2003.
11. M. Ostendorf, P. J. Price, and S. Shattuck-Hufnagel, "The Boston University Radio News Corpus," Tech. Rep. ECS-95-001, Boston University, March 1995.
12. K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, Inc., Boston, second edition, 1990.
13. Y. Bengio, J.-F. Paiement, P. Vincent, O. Delalleau, N. Le Roux, and M. Ouimet, "Out-of-sample extensions for LLE, Isomap, MDS, eigenmaps, and spectral clustering," in Advances in Neural Information Processing Systems. 2004, vol. 16, MIT Press.