Predicting speech recognition confidence using deep learning with word identity and score features

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

Volumn , Issue , 2013, Pages 7413-7417

  Huang, Po Sen ^a   Kumar, Kshitiz ^b   Liu, Chaojun ^b   Gong, Yifan ^b   Deng, Li ^c  

^a University of Illinois at Urbana Champaign   (United States)

^b MICROSOFT   (United States)

^c MICROSOFT RESEARCH   (United States)

Author keywords

Confidence measure; Deep neural network; Kernel deep convex network; Word identity

Indexed keywords

AUTOMATIC SPEECH RECOGNITION; CLASSIFIER PERFORMANCE; CONFIDENCE MEASURE; DEEP CONVEX NETWORKS; DEEP NEURAL NETWORKS; MULTI LAYER PERCEPTRON; RELATIVE PERFORMANCE; WORD IDENTITY;

NEURAL NETWORKS; SPEECH RECOGNITION;

SIGNAL PROCESSING;

EID: 84890479086     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2013.6639103     Document Type: Conference Paper

References (16)

1. D. Yu, J. Li, and L. Deng, "Calibration of confidence measures in speech recognition," IEEE Transactions on Audio, Speech, and Language Processing, vol. 19, no. 8, pp. 2461-2473, Nov. 2011.
2. T. Kemp and T. Schaaf, "Estimating confidence using word lattices," in Proc. of EuroSpeech, 1997, pp. 827-830.
3. F. Wessel, K. Macherey, and H. Ney, "A comparison of word graph and n-best list based confidence measures," in Proc. of EUROSPEECH, 1999, pp. 315-318.
4. H. Jiang, "Confidence measures for speech recognition: A survey," Speech Communication, vol. 45, no. 4, pp. 455-470, 2005.
5. C. White, J. Droppo, A. Acero, and J. Odell, "Maximum entropy confidence estimation for speech recognition," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), April 2007, vol. 4, pp. 809-812.
6. P.-S. Huang, J. Yang, M. Hasegawa-Johnson, F. Liang, and T. S Huang, "Pooling robust shift-invariant sparse representations of acoustic signals," in Interspeech, 2012.
7. G. Hinton and R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, vol. 313, no. 5786, pp. 504-507, 2006
8. G.E. Dahl, D. Yu, L. Deng, and A. Acero, "Contextdependent pre-trained deep neural networks for largevocabulary speech recognition," IEEE Transactions on Audio, Speech, and Language Processing, vol. 20, no. 1, pp. 30-42, Jan. 2012.
9. G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, and B. Kingsbury, "Deep neural networks for acoustic modeling in speech recognition," IEEE Signal Processing Magazine, vol. 29, pp. 82-97, Nov. 2012.
10. L. Deng, G. Tur, X. He, and D. Hakkani-Tur, "Use of kernel deep convex networks and end-to-end learning for spoken language understanding," in IEEE Workshop on Spoken Language Technology, 2012.
11. P.-S. Huang, L. Deng, M. Hasegawa-Johnson, and X. He, "Random features for kernel deep convex network," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2013.
12. L. Deng and D. Yu, "Deep convex network: A scalable architecture for speech pattern classification," in Interspeech, 2011.
13. L. Deng, D. Yu, and J. Platt, "Scalable stacking and learning for building deep architectures," in Intenational Conference on Acoustics, Speech, and Signal Processing, 2012.
14. C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer-Verlag New York, Inc., 2006.
15. P. Drineas and M. W. Mahoney, "On the Nystrom method for approximating a gram matrix for improved kernel-based learning," Journal of Machine Learning Research, vol. 6, pp. 2153-2175, Dec. 2005.
16. C. Cortes, M. Mohri, and A. Talwalkar, "On the impact of kernel approximation on learning accuracy," in International Conference on Artificial Intelligence and Statistics (AISTATS), 2010.