Cited by 226Bayesian Methods and Mixture ModelsSpeech Recognition and SynthesisControl Systems and Identification

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An efficient forward-backward algorithm for an explicit-duration hidden Markov model

Shun-Zheng Yu, Kobayashi, H.·2003·IEEE Signal Processing Letters

DOI 10.1109/lsp.2002.806705

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Abstract

Existing algorithms for estimating the model parameters of an explicit-duration hidden Markov model (HMM) usually require computations as large as O((MD/sup 2/ + M/sup 2/)T) or O(M/sup 2/ DT), where M is the number of states; D is the maximum possible interval between state transitions; and T is the period of observations used to estimate the model parameters. Because of such computational requirements, these algorithms are not practical when we wish to construct an HMM model with large state space and large explicit state duration and process a large amount of measurement data to obtain high accuracy. We propose a new forward-backward algorithm whose computational complexity is only O((MD + M/sup 2/)T), a reduction by almost a factor of D when D > M and whose memory requirement is O(MT). As an application example, we discuss an HMM characterization of access traffic observed at a large-scale Web site: we formulate the Web access pattern in terms of an HMM with explicit duration and estimate the model parameters using our algorithm.

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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
On the self-similar nature of Ethernet traffic (extended version)
Leland, W. E., Taqqu, M. S., Willinger, W., & Wilson, D. V. (1994) · IEEE/ACM Transactions on Networking · doi:10.1109/90.282603 · Cited by 5,232
Continuously variable duration hidden Markov models for automatic speech recognition
Levinson, S. E. (1986) · Computer Speech & Language · doi:10.1016/s0885-2308(86)80009-2 · Cited by 457
On the effect of traffic self-similarity on network performance
Park, K., Kim, G., & Crovella, M. E. (1997) · Cited by 154
Effect of traffic self-similarity on network performance
Park, K., Kim, G., & Crovella, M. E. (1997) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.290419 · Cited by 117
Modeling state durations in hidden Markov models for automatic speech recognition
Ramesh, P., & Wilpon, J. G. (1992) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1992.225892 · Cited by 76
Multiple time scale congestion control for self-similar network traffic
Tuan, T., & Park, K. (1999) · Performance Evaluation · doi:10.1016/s0166-5316(99)00024-3 · Cited by 70
On the complexity of explicit duration HMM's
Mitchell, C., Harper, M., & Jamieson, L. (1995) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.388149 · Cited by 63
A hidden semi-Markov model for web workload self-similarity
Yu, S., Liu, Z., Squillante, M. S., Xia, C. H., & Zhang, L. (2003) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/ipccc.2002.995137 · Cited by 29
Self-citation
On hidden fractal model signal processing
Krishnamurthy, V., Moore, J. B., & Chung, S. (1991) · Signal Processing · doi:10.1016/0165-1684(91)90130-b · Cited by 25

Show all 10 references

Research works printed but not linked: 1

Ferguson, Variable duration models for speech, 143

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Authors: Yu, S.-Z., & Kobayashi, H.
Affiliations: Princeton University (US)
ORCID: Shun-Zheng Yu
Kobayashi, H.
Venue: IEEE Signal Processing Letters
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2003-01
Volume: 10
Issue: 1
Pages: 11-14
ISSN: 1070-9908 (ISSN-L)
1558-2361
DOI: 10.1109/lsp.2002.806705
OpenAlex: W2153331007
Cited by: 226
References: 10
Citations by year: 2026: 1 · 2025: 5 · 2024: 11 · 2023: 6 · 2022: 8 · 2021: 10 · 2020: 11 · 2019: 12 · 2018: 11 · 2017: 16 · 2016: 10 · 2015: 12 · 2014: 19 · 2013: 13 · 2012: 12
FWCI: 5.68
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Signal Processing Letters (publishedVersion)
Funders: George Mason University
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
Keywords: forward-backward algorithm, parameter estimation, computational complexity reduction, space complexity, web traffic modeling, state duration

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An efficient forward-backward algorithm for an explicit-duration hidden Markov model

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