A study of using locality preserving projections for feature extraction in speech recognition

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2008, Pages 1569-1572

  Tang, Yun ^a   Rose, Richard ^a  

^a MCGILL UNIVERSITY   (Canada)

Author keywords

Feature extraction; Locality preserving projections; Manifold learning; Speech recognition

Indexed keywords

ACOUSTICS; DISCRIMINANT ANALYSIS; FACE RECOGNITION; FEATURE EXTRACTION; INFORMATION MANAGEMENT; MANAGEMENT; PRINCIPAL COMPONENT ANALYSIS; REMELTING; RESOURCE ALLOCATION; SIGNAL PROCESSING; SPEECH; SPEECH ANALYSIS;

DIMENSIONALITY REDUCTION ALGORITHMS; LOCALITY PRESERVING PROJECTIONS; MANIFOLD LEARNING; SPEECH RECOGNITION;

ERROR ANALYSIS;

EID: 51449111649     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2008.4517923     Document Type: Conference Paper

References (14)

1. K. Beulen and H. Ney, "Experiments with linear feature extraction in speech recognition," in European Conference on Speech Communication and Technology, Madrid, Spain, 1995, pp. 1415-1418.
2. N. Kumar and A. G. Andreou, "Heteroscedastic discriminant analysis and reduced rank hmms for improved speech recognition," Speech Communication, vol. 26, pp. 283-297, 1998.
3. J. W. Hung and L. S. Lee, "Optimization of temporal filters for constructing robust features in speech recognition," IEEE Transactions on Audio, Speech and language Processing, vol. 14, no. 3, pp. 808 - 832, 2006.
4. V. Jain and L. Saul, "Exploratory analysis and visualization of speech and music by locally linear embedding," in IEEE Proceedings of International Conference on Acoustics, Speech, and Signal Processing, 2004, pp. 984-987.
5. A. Jansen and P. Niyogi, "Intrinsic fourier analysis on the manifold of speech sounds," in IEEE Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Toulouse, France, 2006, pp. 241-244.
6. A. Errity and J. McKenna, "An investigation of manifold learning for speech analysis," in Proceedings of the International Conference on Spoken Language Processing, Pittsburgh, USA, 2006, pp. 2506-2509.
7. 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.
8. S.T. Roweis and L. K. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol. 290, pp. 2323-2326, 2000.
9. M. Belkin and P. Niyogi, "Laplacian eigenmaps and spectral techniques for embedding and clustering," in Advances in Neural Information Processing Systems 14, 2001.
10. X. He and P. Niyogi, "Locality preserving projections," in Advances in Neural Information Processing Systems 16, 2003.
11. X. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang, "Face recognition using laplacianfaces," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328-340, 2005.
12. G. Saon, M. Padmanabhan, R. Gopinath, and S. Chen, "Maximum likelihood discriminant feature spaces," in IEEE Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Istanbul, Turkey, 2000, pp. 1129-1132.
13. P. Somervuo, B. Chen, and Q. Zhu, "Feature transformations and combinations for improving ASR performance," in European Conference on Speech Communication and Technology, Geneva, Switzerland, 2003, pp. 477-480.
14. R. Gopinath, "Maximum likelihood modeling with Gaussian distributions for classification," in IEEE Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Seattle, USA, 1998, pp. 661-664.