Learning label embeddings for nearest-neighbor multi-class classification with an application to speech recognition

Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

Volumn , Issue , 2009, Pages 1678-1686

  Singh Miller, Natasha ^a   Collins, Michael ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTIC MODELING;

ACOUSTICS MODEL; CONDITIONAL PROBABILITIES; EMBEDDINGS; ERROR-CORRECTING OUTPUT CODES; LECTURE RECOGNITION; MULTI-CLASS CLASSIFICATION; NEAREST NEIGHBOURS METHOD; NEAREST-NEIGHBOUR; PROBABILITY ESTIMATE; WORD ERROR RATE;

SPEECH RECOGNITION;

EID: 78650208928     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. E. L. Allwein, R. E. Schapire, and Y. Singer. Reducing multiclass to binary: a unifying approach for margin classifiers. Journal of Machine Learning Research, 1:113-141, 2000.
2. K. Crammer and Y. Singer. Improved output coding for classification using continuous relaxation. In Advances in Neural Information Processing Systems. MIT Press, 2000.
3. K. Crammer and Y. Singer. On the learnability and design of output codes for multiclass problems. Machine Learning, 47(2-3):201-233, 2002.
4. T. G. Dietterich and G. Bakiri. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2:263-286, 1995.
5. J. Glass. A probabilistic framework for segment-based speech recognition. Computer, Speech, and Language, 17(2-3):137-152, 2003.
6. J. Glass, T. J. Hazen, L. Hetherington, and C. Wang. Analysis and processing of lecture audio data: Preliminary investigations. In HLT-NAACL 2004 Workshop on Interdisciplinary Approaches to Speech Indexing and Retrieval, pages 9-12, 2004.
7. A. Globerson and S. Roweis. Metric learning by collapsing classes. In Y. Weiss, B. Scholkopf, and J. Platt, editors, Advances in Neural Information Processing Systems 18, pages 513-520. MIT Press, 2006.
8. J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov. Neighbourhood components analysis. In L. K. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, pages 513-520. MIT Press, 2005.
9. A. Klautau, N. Jevtic, and A. Orlitsky. On nearest-neighbor error-correcting output codes with aplication to all-pairs multiclass support vector machines. Journal of Machine Learning Research, 4:1-15, 2003.
10. W. H. Press, S. A. Teukolsky, W. T. Vetterline, and B. P. Flannery. Numerical recipes: the art of scientific computing. Cambridge University Press, 3 edition, 2007.
11. O. Pujol, P. Radeva, and J. Vitria. Discriminant ecoc: a heuristic method for application dependent design of error correcting output codes. IEEE Transactions of Pattern Analysis and Machine Intelligence, 28(6), 2006.
12. R. Salakhutdinov and G. Hinton. Learning a nonlinear embedding by preserving class neighbourhood structure. AI and Statistics, 2007.
13. N. Singh-Miller, M. Collins, and T. J. Hazen. Dimensionality reduction for speech recognition using neighborhood components analysis. In Interspeech, 2007.
14. A. Torralba, R. Fergus, and Y. Weiss. Small codes and large image databases for recognition. IEEE Computer Vision and Pattern Recognition, June 2008.
15. K. Q. Weinberger, J. Blitzer, and L. K. Saul. Distance metric learning for large margin nearest neighbor classification. In Advances in Neural Information Processing Systems. MIT Press, 2006.
16. G. Zavaliagkos, Y. Zhao, R. Schwartz, and J. Makhoul. A hybrid segmental neural net/hidden markov model system for continuous speech recognition. IEEE Transactions on Speech and Audio Processing, 2(1):151-160, 1994.