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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.”
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“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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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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