An introduction to hidden Markov models
Abstract
The basic theory of Markov chains has been known to mathematicians and engineers for close to 80 years, but it is only in the past decade that it has been applied explicitly to problems in speech processing. One of the major reasons why speech models, based on Markov chains, have not been developed until recently was the lack of a method for optimizing the parameters of the Markov model to match observed signal patterns. Such a method was proposed in the late 1960's and was immediately applied to speech processing in several research institutions. Continued refinements in the theory and implementation of Markov modelling techniques have greatly enhanced the method, leading to a wide range of applications of these models. It is the purpose of this tutorial paper to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Hidden Markov model is a probabilistic model convenient for modeling sequential data.
“Hidden Markov model (HMM) is a probabilistic model which is convenient for modeling sequential data [78].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [78]
Using a hidden Markov model to encode the temporal structure of the sequence of classes obtains a more accurate sequence of predicted classes.
“Therefore we use a hidden Markov model 53 (HMM) to encode the temporal structure of the sequence of classes and thus obtain a more accurate sequence of predicted classes.”
From Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants (2018) · cites this paper as [53]
Cites
Forney, G. D. (1973) · Proceedings of the IEEE · doi:10.1109/proc.1973.9030 · Cited by 5,713 · see citing works
Baum, L. E., Petrie, T., Soules, G., & Weiss, N. (1970) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177697196 · Cited by 4,299 · see citing works
Markel, J. D., & Gray, A. H. (1976) · Communication and cybernetics · doi:10.1007/978-3-642-66286-7 · Cited by 2,258 · see citing works
Baum, L. E., & Eagon, J. A. (1967) · Bulletin of the American Mathematical Society · doi:10.1090/s0002-9904-1967-11751-8 · Cited by 1,053 · see citing works
Levinson, S., Rabiner, L. R., & Sondhi, M. M. (1983) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1983.tb03114.x · Cited by 991 · see citing works
Self-citationContinuous speech recognition by statistical methods
Jelinek, F. (1976) · Proceedings of the IEEE · doi:10.1109/proc.1976.10159 · Cited by 922 · see citing works
The DRAGON system--An overview
Baker, J. (1975) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1975.1162650 · Cited by 521 · see citing works
Mixture autoregressive hidden Markov models for speech signals
Juang, B., & Rabiner, L. (1985) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1985.1164727 · Cited by 381 · see citing works
Self-citationRabiner, L. R., Levinson, S., & Sondhi, M. M. (1983) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1983.tb03115.x · Cited by 310 · see citing works
Self-citationLinear predictive hidden Markov models and the speech signal
Poritz, A. B. (1982) · IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1982.1171633 · Cited by 272 · see citing works
Recognition of Isolated Digits Using Hidden Markov Models With Continuous Mixture Densities
Rabiner, L. R., Juang, B.-H., Levinson, S., & Sondhi, M. M. (1985) · AT&T Technical Journal · doi:10.1002/j.1538-7305.1985.tb00272.x · Cited by 242 · see citing works
Self-citationOn the Hidden Markov Model and Dynamic Time Warping for Speech Recognition-A Unified View
Juang, B.-H. (1984) · AT&T Bell Laboratories Technical Journal · doi:10.1002/j.1538-7305.1984.tb00034.x · Cited by 147 · see citing works
Self-citationConnected digit recognition using vector quantization
Bourlard, H., Wellekens, C. J., & Ney, H. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1984.1172585 · Cited by 19 · see citing works
On the cryptanalysis of rotor machines and substitution - permutation networks
Andelman, D., & Reeds, J. A. (1982) · IEEE Transactions on Information Theory · doi:10.1109/tit.1982.1056523 · Cited by 14 · see citing works
Markov Models for Phonetic Text
Neuburg, E. P. (1971) · The Journal of the Acoustical Society of America · doi:10.1121/1.1977512 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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 · see citing works
Self-citationRecognizing human action in time-sequential images using hidden Markov model
Yamato, J., Ohya, J., & Ishii, K. (2003) · Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1992.223161 · Cited by 1,273 · see citing works
Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
Cao, J., Pang, J., Weng, X., Khirodkar, R., & Kitani, K. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00934 · Cited by 1,115 · see citing works
The hierarchical hidden Markov model: Analysis and applications
Fine, S., Singer, Y., & Tishby, N. (1998) · Machine Learning · doi:10.1023/a:1007469218079 · Cited by 906 · see citing works
Deep learning in video multi-object tracking: A survey
Ciaparrone, G., Luque Sánchez, F., Tabik, S., Troiano, L., Tagliaferri, R., & Herrera, F. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.11.023 · Cited by 665 · see citing works
Speech recognition in noisy environments: A survey
Gong, Y. (1995) · Speech Communication · doi:10.1016/0167-6393(94)00059-j · Cited by 635 · see citing works
Modeling and Prediction of Human Behavior
Pentland, A., & Liu, A. (1999) · Neural Computation · doi:10.1162/089976699300016890 · Cited by 520 · see citing works
Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452 · see citing works
GWAS identifies 14 loci for device-measured physical activity and sleep duration
Doherty, A., Smith-Byrne, K., Ferreira, T., Holmes, M. V., Holmes, C., Pulit, S. L., & Lindgren, C. M. (2018) · Nature Communications · doi:10.1038/s41467-018-07743-4 · Cited by 408 · see citing works
Artificial intelligence, cyber-threats and industry 4.0: challenges and opportunities
Bécue, A., Praça, I., & Gama, J. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-020-09942-2 · Cited by 368 · see citing works
Bayesian Spiking Neurons I: Inference
Deneve, S. (2008) · Neural Computation · doi:10.1162/neco.2008.20.1.91 · Cited by 313 · see citing works
Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex
Rao, R. P. N., & Ballard, D. H. (1997) · Neural Computation · doi:10.1162/neco.1997.9.4.721 · Cited by 290 · see citing works
Information retrieval and text mining technologies for chemistry
Krallinger, M., Rabal, O., Lourenço, A., Oyarzabal, J., & Valencia, A. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00851 · Cited by 281 · see citing works
Willetts, M., Hollowell, S., Aslett, L., Holmes, C., & Doherty, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-26174-1 · Cited by 250 · see citing works
“To predict the MET score we follow the same process of feature extraction, random forest training and HMM definition, but for the eleven-class MET-relevant behaviour labels.”
MethodHardware Design of Smart Home Energy Management System With Dynamic Price Response
Hu, Q., & Li, F. (2013) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2013.2258181 · Cited by 261 · see citing works
Bayesian Computation in Recurrent Neural Circuits
Rao, R. P. N. (2004) · Neural Computation · doi:10.1162/08997660460733976 · Cited by 252 · see citing works
Assessment of Tremor Activity in the Parkinson’s Disease Using a Set of Wearable Sensors
Rigas, G., Tzallas, A. T., Tsipouras, M. G., Bougia, P., Tripoliti, E. E., Baga, D., Fotiadis, D. I., Tsouli, S. G., & Konitsiotis, S. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2011.2182616 · Cited by 241 · see citing works
Automated facial expression recognition based on FACS action units
Lien, J. J., Kanade, T., Cohn, J. F., & Li, C.-C. (2002) · Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.1998.670980 · Cited by 226 · see citing works
Automatic classification of sentences to support Evidence Based Medicine.
Kim, S. N., Martinez, D., Cavedon, L., & Yencken, L. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-s2-s5 · Cited by 214 · see citing works
A survey on neural speech synthesis
Tan, X., Qin, T., Soong, F., & Liu, T.-Y. (2021) · arXiv · doi:10.48550/arxiv.2106.15561 · Cited by 216 · see citing works
Hip and Wrist Accelerometer Algorithms for Free-Living Behavior Classification
ELLIS, K., KERR, J., GODBOLE, S., STAUDENMAYER, J., & LANCKRIET, G. (2016) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000840 · Cited by 186 · see citing works
BILMES, J. A. (2006) · IEICE Transactions on Information and Systems · doi:10.1093/ietisy/e89-d.3.869 · Cited by 172 · see citing works
Detection and diagnosis of bearing and cutting tool faults using hidden Markov models
Boutros, T., & Liang, M. (2011) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2011.01.013 · Cited by 155 · see citing works
Progress in context-aware recommender systems — An overview
Raza, S., & Ding, C. (2019) · Computer Science Review · doi:10.1016/j.cosrev.2019.01.001 · Cited by 151 · see citing works
A Comprehensive Study of Activity Recognition Using Accelerometers
Twomey, N., Diethe, T., Fafoutis, X., Elsts, A., McConville, R., Flach, P., & Craddock, I. (2018) · Informatics · doi:10.3390/informatics5020027 · Cited by 143 · see citing works
Neural network models for software development effort estimation: a comparative study
Nassif, A. B., Azzeh, M., Capretz, L. F., & Ho, D. (2016) · Neural Computing and Applications · doi:10.1007/s00521-015-2127-1 · Cited by 122 · see citing works
Xu, J., Pan, S., Sun, P. Z. H., Hyeong Park, S., & Guo, K. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3225782 · Cited by 120 · see citing works
A voice-input voice-output communication aid for people with severe speech impairment
Hawley, M. S., Cunningham, S. P., Green, P. D., Enderby, P., Palmer, R., Sehgal, S., & O'Neill, P. (2013) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2209678 · Cited by 110 · see citing works
Gait phase analysis based on a Hidden Markov Model
Bae, J., & Tomizuka, M. (2011) · Mechatronics · doi:10.1016/j.mechatronics.2011.03.003 · Cited by 106 · see citing works
Artificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions
Kirubarajan, A., Young, D., Khan, S., Crasto, N., Sobel, M., & Sussman, D. (2022) · Journal of Surgical Education · doi:10.1016/j.jsurg.2021.09.012 · Cited by 101 · see citing works
Betthauser, J. L., Krall, J. T., Bannowsky, S. G., Levay, G., Kaliki, R. R., Fifer, M. S., & Thakor, N. V. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2943309 · Cited by 73 · see citing works
Machine learning in materials design: Algorithm and application*
Song, Z., Chen, X., Meng, F., Cheng, G., Wang, C., Sun, Z., & Yin, W.-J. (2020) · Chinese Physics B · doi:10.1088/1674-1056/abc0e3 · Cited by 63 · see citing works
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 · see citing works
Time2Vec: Learning a Vector Representation of Time
Kazemi, S. M., Goel, R., Eghbali, S., Ramanan, J., Sahota, J., Thakur, S., Wu, S., Smyth, C., Poupart, P., & Brubaker, M. (2019) · arXiv · doi:10.48550/arxiv.1907.05321 · Cited by 206 · see citing works
A Comprehensive Study of Activity Recognition Using Accelerometers
Twomey, N., Diethe, T., Fafoutis, X., Elsts, A., McConville, R., Flach, P., & Craddock, I. (2018) · doi:10.20944/preprints201803.0147.v1 · Cited by 143 · see citing works
Barnes, S. J. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106967 · Cited by 23 · see citing works
“A Hidden Markov Model (HMM) is a dual stochastic process containing an unobservable, latent or ‘hidden ’ stochastic process that can only be observed via an alternative set of stochastic processes and the symbols they produce (Murphy, 2012; Rabiner & Wang, 1986).”
BackgroundEstimating and Penalizing Preference Shift in Recommender Systems
Carroll, M., Hadfield-Menell, D., Russell, S., & Dragan, A. (2021) · Fifteenth ACM Conference on Recommender Systems · doi:10.1145/3460231.3478849 · Cited by 20 · see citing works
Kadi, H. A., & Terzić, K. (2023) · Sensors · doi:10.3390/s23052389 · Cited by 19 · see citing works
“Hidden Markov Model (HMM) [71] is a first-order Markov process whose internal states S are not directly observable.”
BackgroundA Computational Model of Coupled Human Trust and Self-confidence Dynamics
Williams, K. J., Yuh, M. S., & Jain, N. (2023) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3594715 · Cited by 18 · see citing works
Meta-Transfer Learning-Based Handover Optimization for V2N Communication
Sohaib, R. M., Onireti, O., Tan, K., Sambo, Y., Swash, R., & Imran, M. A. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3431875 · Cited by 15 · see citing works
Mohan, C., Gleeson, T., Famiglietti, J. S., Virkki, V., Kummu, M., Porkka, M., Huggins, X., Wang-Erlandsson, L., Gerten, D., & Jähnig, S. C. (2022) · doi:10.1002/essoar.10507838.2 · Cited by 14 · see citing works
Hong, A. E., Malinovsky, P. P., & Damodaran, S. K. (2024) · Digital Threats: Research and Practice · doi:10.1145/3604434 · Cited by 11 · see citing works
McMahon, G., Akash, K., Reid, T., & Jain, N. (2020) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2021.04.113 · Cited by 10 · see citing works
- Authors
- Rabiner, L., & Juang, B.
- Affiliations
- AT&T (United States) (US)
- ORCID
- Juang, B.
- Venue
- IEEE ASSP Magazine
- Published
- 1986
- Volume
- 3
- Issue
- 1
- Pages
- 4-16
- ISSN
- 0740-7467 (ISSN-L)
- OpenAlex
- W2105594594
- Cited by
- 4,775
- References
- 15
- Citations by year
- 2026: 73 · 2025: 137 · 2024: 174 · 2023: 186 · 2022: 211 · 2021: 222 · 2020: 198 · 2019: 213 · 2018: 208 · 2017: 238 · 2016: 225 · 2015: 200 · 2014: 190 · 2013: 177 · 2012: 181
- FWCI
- 12.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE ASSP Magazine (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Keywords
- hidden Markov models, Markov chains, speech recognition, parameter estimation, speech models
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
