The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Multi-style training for robust isolated-word speech recognition
Abstract
A new training procedure called multi-style training has been developed to improve performance when a recognizer is used under stress or in high noise but cannot be trained in these conditions. Instead of speaking normally during training, talkers use different, easily produced, talking styles. This technique was tested using a speech data base that included stress speech produced during a workload task and when intense noise was presented through earphones. A continuous-distribution talker-dependent Hidden Markov Model (HMM) recognizer was trained both normally (5 normally spoken tokens) and with multi-style training (one token each from normal, fast, clear, loud, and question-pitch talking styles). The average error rate under stress and normal conditions fell by more than a factor of two with multi-style training and the average error rate under conditions sampled during training fell by a factor of four.
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Cites
On the mean accuracy of statistical pattern recognizers
Hughes, G. (1968) · IEEE Transactions on Information Theory · doi:10.1109/tit.1968.1054102 · Cited by 2,859
Levinson, S. E., Rabiner, L. R., & Sondhi, M. M. (1983) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1983.tb03114.x · Cited by 978
Cepstral domain talker stress compensation for robust speech recognition
Chen, Y. (1988) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/29.1547 · Cited by 98
A speaker-stress resistant HMM isolated word recognizer
Paul, D. B. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1987.1169551 · Cited by 53
Self-citation
Cited by
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Automatic speech recognition and speech variability: A review
Benzeghiba, M., De Mori, R., Deroo, O., Dupont, S., Erbes, T., Jouvet, D., Fissore, L., Laface, P., Mertins, A., Ris, C., et al. (2007) · Speech Communication · doi:10.1016/j.specom.2007.02.006 · Cited by 529
Nonlinear feature based classification of speech under stress.
Zhou, G., Hansen, J. H. L., & Kaiser, J. F. (2001) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.905995 · Cited by 409
Data Augmentation for Deep Neural Network Acoustic Modeling
Cui, X., Goel, V., & Kingsbury, B. (2015) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2015.2438544 · Cited by 376
A study on speaker adaptation of the parameters of continuous density hidden Markov models
Lee, C.-H., Lin, C.-H., & Juang, B.-H. (1991) · IEEE Transactions on Signal Processing · doi:10.1109/78.80902 · Cited by 262
Metadata
- Authors
- Lippmann, R., Martin, E., & Paul, D.
- Venue
- ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing
- Published
- 2005-03-24
- Volume
- 12
- Pages
- 705-708
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2136439176
- Cited by
- 309
- References
- 4
- FWCI
- 9.06
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Lippmann, R., Martin, E., & Paul, D. (2005). Multi-style training for robust isolated-word speech recognition. ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing. https://doi.org/10.1109/icassp.1987.1169544
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