The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Speech recognition with continuous-parameter hidden Markov models
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Claims the paper makes
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“On the other hand, if the probabilistic assumptions in an HMM (or other probabilistic model) are not realistic, discriminative training, discussed in Section VI, can improve performance as this has been clearly shown for speech recognition systems [48]- [50], [107], [108].”
From Gradient-based learning applied to document recognition (1998) · cites this paper as [49]
Citations
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
Self-citation
Flagged first, then by citations
Research works printed but not linked: 9
- Bahl, 1983, A maximum likelihood approach to continuous speech recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-5, 179, 10.1109/TPAMI.1983.4767370 · DOI
- Baum, 1972, An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process, Inequalities, 3, 1
- Brown, 1987, The Acoustic-Modeling Problem in Automatic Speech Recognition, 10.21236/ADA188529 · DOI
- Cohen, 1988, Application of an auditory model to speech recognition, Journal of the Acoustic Society of America
- Friedman, 1967, On some invariant criteria for grouping data, American Statistical Association Journal, 1159, 10.2307/2283767 · DOI
- Gray, 1984, Vector quantization, IEEE ASSP Magazine, 1, 4, 10.1109/MASSP.1984.1162229 · DOI
- Nadas, 1983, A decision-theoretic formulation of a training problem in speech recognition and a comparison of training by unconditional versus conditional maximum likelihood, IEEE Transactions on Acoustics, Speech and Signal Processing, ASSP-31, 814, 10.1109/TASSP.1983.1164173 · DOI
- Patterson, N. (1986). Personal communication. · DOI
- Rabiner, 1985, Recognition of isolated digits using hidden Markov models with continuous mixture densities, Bell Systems Technical Journal, 64, 1211
Cited by
Sorted by citations
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Metadata
- Authors
- Bahl, L. R., Brown, P. F., de Souza, P. V., & Mercer, R. L.
- Affiliations
- IBM Research - Thomas J. Watson Research Center (US)
- Venue
- Computer Speech & Language
- Publisher
- Elsevier BV
- Published
- 1987-09
- Volume
- 2
- Issue
- 3-4
- Pages
- 219-234
- ISSN
- 0885-2308 (ISSN-L)
- OpenAlex
- W2053176763
- Cited by
- 28
- References
- 1
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2022: 1 · 2021: 1 · 2016: 1
- FWCI
- 0.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Speech & Language (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- speech recognition, hidden Markov models, acoustic modeling, information-theoretic analysis, parameter estimation, model accuracy, information extraction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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