The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Minimum Phone Error and I-smoothing for improved discriminative training
Abstract
In this paper we introduce the Minimum Phone Error (MPE) and Minimum Word Error (MWE) criteria for the discriminative training of HMM systems. The MPE/MWE criteria are smoothed approximations to the phone or word error rate respectively. We also discuss I-smoothing which is a novel technique for smoothing discriminative training criteria using statistics for maximum likelihood estimation (MLE). Experiments have been performed on the Switchboard/Call Home corpora of telephone conversations with up to 265 hours of training data. It is shown that for the maximum mutual information estimation (MMIE) criterion, I-smoothing reduces the word error rate (WER) by 0.4% absolute over the MMIE baseline. The combination of MPE and I-smoothing gives an improvement of 1 % over MMIE and a total reduction in WER of 4.8% absolute over the original MLE system.
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Cites
Maximum mutual information estimation of hidden Markov model parameters for speech recognition
Bahl, L., Brown, P., de Souza, P., & Mercer, R. (2005) · ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1986.1169179 · Cited by 817
Minimum classification error rate methods for speech recognition
Juang, B.-H., Hou, W., & Lee, C.-H. (1997) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.568732 · Cited by 618
Large scale discriminative training of hidden Markov models for speech recognition
Woodland, P. C., & Povey, D. (2002) · Computer Speech & Language · doi:10.1006/csla.2001.0182 · Cited by 306
Self-citationAn inequality for rational functions with applications to some statistical estimation problems
Gopalakrishnan, P. S., Kanevsky, D., Nadas, A., & Nahamoo, D. (1991) · IEEE Transactions on Information Theory · doi:10.1109/18.61108 · Cited by 197
MMIE training of large vocabulary recognition systems
Valtchev, V, Odell, J. J., Woodland, P. C., & Young, S. J. (1997) · Speech Communication · doi:10.1016/s0167-6393(97)00029-0 · Cited by 175
Self-citationHidden Markov models, maximum mutual information estimation, and the speech recognition problem
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Improved discriminative training techniques for large vocabulary continuous speech recognition
Povey, D., & Woodland, P. C. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2001.940763 · Cited by 47
Self-citationDecoder selection based on cross-entropies
Gopalakrishnan, P. S., Kanevsky, D., Nadas, A. J., Nahamoo, D., & Picheny, M. (2003) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1988.196499 · Cited by 38
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Cited by
Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2134090 · Cited by 3,011
Deep Learning: Methods and Applications
Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,759
State-of-the-Art Speech Recognition with Sequence-to-Sequence Models
Chiu, C.-C., Sainath, T. N., Wu, Y., Prabhavalkar, R., Nguyen, P., Chen, Z., Kannan, A., Weiss, R. J., Rao, K., Gonina, E., et al. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8462105 · Cited by 1,043
Hidden conditional random fields for phone classification
Gunawardana, A., Mahajan, M., Acero, A., & Platt, J. C. (2005) · Interspeech 2005 · doi:10.21437/interspeech.2005-126 · Cited by 299
Fundamentals of speech recognition
Li, J., Deng, L., Haeb-Umbach, R., & Gong, Y. (2016) · Robust Automatic Speech Recognition · doi:10.1016/b978-0-12-802398-3.00002-7 · Cited by 266
Metadata
- Authors
- Povey, D., & Woodland, P. C.
- Venue
- IEEE International Conference on Acoustics Speech and Signal Processing
- Publisher
- IEEE
- Published
- 2002-05
- Pages
- I-105-I-108
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2150907703
- Cited by
- 622
- References
- 9
- FWCI
- 8.43
- Type
- conference paper
- Language
- en
- Open access
- closed
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