Cited by 906Speech Recognition and SynthesisBayesian Methods and Mixture ModelsNatural Language Processing Techniques

The hierarchical hidden Markov model: Analysis and applications

Open access
Fine, Shai, Singer, Yoram, Tishby, Naftali·1998·Machine Learning

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Registry

Crossref · 10.1023/a:1007469218079

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32 of 32 references verified0 citation errors · 0 not checkable

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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.

    “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.”
  • In a two-layer HHMM, the duration of a bottom-level state i in ch(p) follows a geometric distribution.

    “In this two-layer HHMM, the duration of a bottom-level state i ∈ ch(p), denoted as D p,i , follows a geometric distribution.”

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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
32
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
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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