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Maximum mutual information estimation of hidden Markov model parameters for speech recognition
Abstract
A method for estimating the parameters of hidden Markov models of speech is described. Parameter values are chosen to maximize the mutual information between an acoustic observation sequence and the corresponding word sequence. Recognition results are presented comparing this method with maximum likelihood estimation.
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Baum, L. E. (1972) · Medical Entomology and Zoology · Cited by 1,405
A Maximum Likelihood Approach to Continuous Speech Recognition
Bahl, L. R., Jelinek, F., & Mercer, R. L. (1983) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1983.4767370 · Cited by 1,220
Self-citationSome experiments with large-vocabulary isolated-word sentence recognition
Bahl, L. R., Das, S. K., de Souza, P., Jelinek, F., Katz, S., Mercer, R. L., & Picheny, M. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1984.1172520 · Cited by 19
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Zhou, X., Zhang, Y., Tian, F., Wang, H., & Liu, C. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.12.002 · Cited by 28
Pargett, M., & Umulis, D. M. (2013) · Methods · doi:10.1016/j.ymeth.2013.03.024 · Cited by 28
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Quantifying information of intracellular signaling: progress with machine learning
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Translating collocations for bilingual lexicons
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Maaskola, J., & Rajewsky, N. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1083 · Cited by 27
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Mooney, R. J., & Bilenko, M. (2006) · Cited by 24
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Mallory, J. R., Stinson, M., Elliot, L. B., & Easton, D. (2017) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3132525.3134779 · Cited by 22
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Ney, H. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-44871-6_74 · Cited by 19
Penalized Logistic Regression With HMM Log-Likelihood Regressors for Speech Recognition
Birkenes, Ø., Matsui, T., Tanabe, K., Siniscalchi, S. M., Myrvoll, T. A., & Johnsen, M. H. (2009) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2009.2035151 · Cited by 20
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de la Torre, Á., Peinado, A. M., Rubio, A. J., Segura, J. C., & Benı́tez, C. (2002) · Speech Communication · doi:10.1016/s0167-6393(01)00068-1 · Cited by 18
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Gillick, D., Wegmann, S., & Gillick, L. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6288979 · Cited by 17
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Young, S. J. (1999) · Springer eBooks · doi:10.1007/978-3-642-60087-6_3 · Cited by 17
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Smadja, F., & McKeown, K. R. (1994) · DARPA Workshop on Speech and Natural Language (HLT) · doi:10.3115/1075812.1075843 · Cited by 17
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Tachioka, Y., Watanabe, S., & Hershey, J. R. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639006 · Cited by 16
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Li, J., Siniscalchi, S. M., & Lee, C. (2007) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2007.366997 · Cited by 16
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Lee, C., & Juang, B.-H. F. (1996) · doi:10.30019/ijclclp.199608.0001 · Cited by 15
Metadata
- Authors
- Bahl, L., Brown, P., de Souza, P., & Mercer, R.
- Affiliations
- IBM Research - Thomas J. Watson Research Center (US)
- Venue
- ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing
- Published
- 2005-03-24
- Volume
- 11
- Pages
- 49-52
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W1877570817
- Cited by
- 817
- References
- 3
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 13 · 2023: 11 · 2022: 16 · 2021: 30 · 2020: 25 · 2019: 30 · 2018: 33 · 2017: 24 · 2016: 25 · 2015: 34 · 2014: 42 · 2013: 29 · 2012: 39
- FWCI
- 28.55
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Phonetics and Phonology Research
- Keywords
- hidden Markov models, parameter estimation, mutual information maximization, speech recognition, word sequences, maximum likelihood estimation comparison
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