The DOI matches its Crossref record; 32 of the 33 references it cites were found in a registry or an index.
The Hierarchical Hidden Markov Model: Analysis and Applications
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“Fine et al. [10] were the first to introduce the HHMM, generalizing the HMM by viewing each state as an autonomous probabilistic HMM model itself.”
From Efficient duration and hierarchical modeling for human activity recognition (2009) · cites this paper as [10]
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In the citing paper
“In this two-layer HHMM, the duration of a bottom-level state i ∈ ch(p), denoted as D p,i , follows a geometric distribution.”
From Efficient duration and hierarchical modeling for human activity recognition (2009) · cites this paper as [10]
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Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 37,139
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
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
Viterbi, A. J. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1054010 · Cited by 6,954
An introduction to hidden Markov models
Rabiner, L., & Juang, B. (1986) · IEEE ASSP Magazine · doi:10.1109/massp.1986.1165342 · Cited by 4,775
Statistical Inference for Probabilistic Functions of Finite State Markov Chains
Baum, L. E., & Petrie, T. (1966) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177699147 · Cited by 2,775
Unsupervised word sense disambiguation rivaling supervised methods
Yarowsky, D. (1995) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/981658.981684 · Cited by 2,411
A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition
Rabiner, L. R. (1990) · Elsevier eBooks · doi:10.1016/b978-0-08-051584-7.50027-9 · Cited by 1,945
Elements of Information Theory
Phoenix, S. J. D. (1992) · Journal of Modern Optics · doi:10.1080/09500349214551641 · Cited by 1,442
Factorial Hidden Markov Models
Ghahramani, Z., & Jordan, M. I. (1997) · Machine Learning · doi:10.1023/a:1007425814087 · Cited by 1,157
Flagged first, then by citations
1 of the 33 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Dynamic bayesian networks: representation, inference and learning
Murphy, K. P., & Russell, S. (2002) · Cited by 2,579
Stochastic Variational InferencePreprint
Hoffman, M. D., Blei, D. M., Wang, C., & Paisley, J. W. (2012) · arXiv (Cornell University) · doi:10.5555/2567709.2502622 · Cited by 1,991
Deep Learning: Methods and Applications
Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,789
Bursty and Hierarchical Structure in Streams
Kleinberg, J. M. (2003) · Data Mining and Knowledge Discovery · doi:10.1023/a:1024940629314 · Cited by 1,372
Metadata
- Authors
- Fine, S., Singer, Y., & Tishby, N.
- Affiliations
- Hebrew University of Jerusalem (IL)
- AT&T (United States) (US)
- Venue
- Machine Learning
- Published
- 1998-07
- Volume
- 32
- Issue
- 1
- Pages
- 41-62
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1636244751
- Cited by
- 906
- References
- 33
- Citations by year
- 2026: 10 · 2025: 17 · 2024: 19 · 2023: 26 · 2022: 39 · 2021: 36 · 2020: 51 · 2019: 34 · 2018: 30 · 2017: 39 · 2016: 33 · 2015: 31 · 2014: 53 · 2013: 36 · 2012: 43
- FWCI
- 3.53
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- hierarchical hidden Markov models, unsupervised parameter estimation, Baum-Welch algorithm, cursive script, natural language modeling
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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.
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