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The viterbi algorithm
Abstract
The Viterbi algorithm (VA) is a recursive optimal solution to the problem of estimating the state sequence of a discrete-time finite-state Markov process observed in memoryless noise. Many problems in areas such as digital communications can be cast in this form. This paper gives a tutorial exposition of the algorithm and of how it is implemented and analyzed. Applications to date are reviewed. Increasing use of the algorithm in a widening variety of areas is foreseen.
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Cited by
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
An introduction to hidden Markov models
Rabiner, L., & Juang, B. (1986) · IEEE ASSP Magazine · doi:10.1109/massp.1986.1165342 · Cited by 4,775
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332
Near optimum error correcting coding and decoding: turbo-codes
Berrou, C., & Glavieux, A. (1996) · IEEE Transactions on Communications · doi:10.1109/26.539767 · Cited by 2,761
AMASS: Archive of Motion Capture As Surface Shapes
Mahmood, N., Ghorbani, N., Troje, N. F., Pons-Moll, G., & Black, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00554 · Cited by 1,323
Metadata
- Authors
- Forney, G. D.
- Venue
- Proceedings of the IEEE
- Published
- 1973
- Volume
- 61
- Issue
- 3
- Pages
- 268-278
- ISSN
- 0018-9219 (ISSN-L)
- OpenAlex
- W2142384583
- Cited by
- 5,647
- References
- 0
- FWCI
- 32.21
- Type
- journal article
- Language
- en
- Open access
- closed
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- Forney, G. D. (1973). The viterbi algorithm. Proceedings of the IEEE. https://doi.org/10.1109/proc.1973.9030
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
