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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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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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