The DOI matches its Crossref record.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Diagnostic genome profiling in mental retardation
de Vries, B. B. A., Pfundt, R., Leisink, M., Koolen, D. A., Vissers, L. E. L. M., Janssen, I. M., Reijmersdal, S. V., Nillesen, W. M., Huys, E. H. L. P. G., Leeuw, N. D., et al. (2005) · The American Journal of Human Genetics · doi:10.1086/491719 · Cited by 605
Classification in Networked Data: A Toolkit and a Univariate Case Study
(2007) · Journal of Machine Learning Research · doi:10.5555/1248659.1248693 · Cited by 452
Anipose: A toolkit for robust markerless 3D pose estimation
Karashchuk, P., Rupp, K. L., Dickinson, E. S., Walling-Bell, S., Sanders, E., Azim, E., Brunton, B. W., & Tuthill, J. C. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109730 · Cited by 324
Willetts, M., Hollowell, S., Aslett, L., Holmes, C., & Doherty, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-26174-1 · Cited by 250
“Finally, the predicted MET score for each 30-second chunk is calculated as the assigned MET scores for each of the 11 states, weighted by the marginal probabilities of being in each of those states.”
MethodWalmsley, R., Chan, S., Smith-Byrne, K., Ramakrishnan, R., Woodward, M., Rahimi, K., Dwyer, T., Bennett, D., & Doherty, A. (2022) · British Journal of Sports Medicine · doi:10.1136/bjsports-2021-104050 · Cited by 238
A New Take on Detecting Insider Threats: Exploring the Use of Hidden Markov Models
Rashid, T., Agrafiotis, I., & Nurse, J. R. C. (2016) · Proceedings of the 8th ACM CCS International Workshop on Managing Insider Security Threats · doi:10.1145/2995959.2995964 · Cited by 152
Quantum Search Algorithms for Wireless Communications
Botsinis, P., Alanis, D., Babar, Z., Nguyen, H. V., Chandra, D., Ng, S. X., & Hanzo, L. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2882385 · Cited by 149
(2018) · doi:10.1016/j.asoc.2017.06.035 · Cited by 141
Bimodal Distribution and Co-Bursting in Review Spam Detection
Li, H., Fei, G., Wang, S., Liu, B., Shao, W., Mukherjee, A., & Shao, J. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052582 · Cited by 141
Interacting with computers by voice: automatic speech recognition and synthesis
O'Shaughnessy, D. (2003) · Proceedings of the IEEE · doi:10.1109/jproc.2003.817117 · Cited by 132
Single-chain heteropolymers transport protons selectively and rapidly
Jiang, T., Hall, A., Eres, M., Hemmatian, Z., Qiao, B., Zhou, Y., Ruan, Z., Couse, A. D., Heller, W. T., Huang, H., et al. (2020) · Nature · doi:10.1038/s41586-019-1881-0 · Cited by 122
Clemins, P. J., Johnson, M. T., Leong, K. M., & Savage, A. (2005) · The Journal of the Acoustical Society of America · doi:10.1121/1.1847850 · Cited by 115
Zero-Effort In-Home Sleep and Insomnia Monitoring using Radio Signals
Hsu, C.-Y., Ahuja, A., Yue, S., Hristov, R., Kabelac, Z., & Katabi, D. (2017) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3130924 · Cited by 97
An Impulse Radio Ultrawideband System for Contactless Noninvasive Respiratory Monitoring
Nijsure, Y., Tay, W. P., Gunawan, E., Wen, F., Yang, Z., Guan, Y. L., & Chua, A. P. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2237401 · Cited by 74
PICO Element Detection in Medical Text via Long Short-Term Memory Neural Networks.
Jin, D., & Szolovits, P. (2018) · Proceedings of the BioNLP 2018 workshop · doi:10.18653/v1/w18-2308 · Cited by 61
Fuhs, M. C., VanRhoads, S. R., Casale, A. E., McNaughton, B., & Touretzky, D. S. (2005) · Journal of Neurophysiology · doi:10.1152/jn.00132.2005 · Cited by 53
Untangling serially dependent underreported count data for gender-based violence
Fernández‐Fontelo, A., Cabaña, A., Joe, H., Puig, P., & Moriña, D. (2019) · Statistics in Medicine · doi:10.1002/sim.8306 · Cited by 47
How data-driven entrepreneur analyzes imperfect information for business opportunity evaluation
Canakoglu, E., Erzurumlu, S. S., & Erzurumlu, Y. O. (2018) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2018.2826983 · Cited by 47
Sharma, H., Drukker, L., Chatelain, P., Droste, R., Papageorghiou, A. T., & Noble, J. A. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101973 · Cited by 48
Advancing PICO element detection in biomedical text via deep neural networks.
Jin, D., & Szolovits, P. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa256 · Cited by 41
Shortest path segmentation: A method for training a neural network to recognize character strings
Burges, C. J. C., Matan, O., Le Cun, Y., Denker, J. S., Jackel, L. D., Stenard, C. E., Nohl, C. R., & Ben, J. I. (2003) · [Proceedings 1992] IJCNN International Joint Conference on Neural Networks · doi:10.1109/ijcnn.1992.227175 · Cited by 37
Elhadi, M., & Al-Tobi, A. (2009) · 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology · doi:10.1109/iccit.2009.235 · Cited by 32
Panayi, A., Ward, K., Benhadji-Schaff, A., Ibanez-Lopez, A. S., Xia, A., & Barzilay, R. (2023) · Systematic Reviews · doi:10.1186/s13643-023-02351-w · Cited by 23
Accurate Protein-Folding Transition-Path Statistics from a Simple Free-Energy Landscape
Jacobs, W. M., & Shakhnovich, E. I. (2018) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.8b05842 · Cited by 19
Towards optimal outsourcing of service function chain across multiple clouds
Chen, H., Xu, S., Wang, X., Zhao, Y., Li, K., Wang, Y., Wang, W., & Li, L. M. (2016) · 2016 IEEE International Conference on Communications (ICC) · doi:10.1109/icc.2016.7510996 · Cited by 17
Giri, N., Wang, L., & Cheng, J. (2024) · Scientific Data · doi:10.1038/s41597-024-03299-9 · Cited by 17
A Framework for Estimating Long Term Driver Behavior
Gadepally, V., Krishnamurthy, A., & Özgüner, Ü. (2017) · Journal of Advanced Transportation · doi:10.1155/2017/3080859 · Cited by 17
Walmsley, R., Chan, S., Smith-Byrne, K., Ramakrishnan, R., Woodward, M., Rahimi, K., Dwyer, T., Bennett, D., & Doherty, A. (2020) · doi:10.1101/2020.11.10.20227769 · Cited by 12
Automated invoice processing: Machine learning-based information extraction for long tail suppliers
Krieger, F., Drews, P., & Funk, B. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200285 · Cited by 11
Triple network dynamics and future alcohol consumption in adolescents
McIntyre, C. C., Khodaei, M., Lyday, R. G., Weiner, J. L., Laurienti, P. J., & Shappell, H. M. (2025) · Alcohol, Clinical and Experimental Research · doi:10.1111/acer.70043 · Cited by 5
Connecting the dots: deep learning-based automated model building methods in cryo-EM
Bansia, H., & des Georges, A. (2026) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2025.1613399 · Cited by 2
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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
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.
