Hidden semi-Markov models

Maximum Likelihood from Incomplete Data Via the EM Algorithm

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444

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

The EM Algorithm and Extensions

Kushary, D. (1998) · Technometrics · doi:10.1080/00401706.1998.10485534 · Cited by 5,417

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 5,233

Prediction of complete gene structures in human genomic DNA

Burge, C., & Karlin, S. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.0951 · Cited by 4,228

Statistical Inference for Probabilistic Functions of Finite State Markov Chains

Baum, L. E., & Petrie, T. (1966) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177699147 · Cited by 2,775

GeneMark.hmm: new solutions for gene finding

Lukashin, A. (1998) · Nucleic Acids Research · doi:10.1093/nar/26.4.1107 · Cited by 1,538

Inference in Hidden Markov Models

Cappé, O., Moulines, E., & Rydén, T. (2005) · Springer Series in Statistics · doi:10.1007/0-387-28982-8 · Cited by 1,398

Inference in Hidden Markov Models

Capp, O., Moulines, É., & Rydèn, T. (2010) · Springer eBooks · Cited by 1,108

An Introduction to the Application of the Theory of Probabilistic Functions of a Markov Process to Automatic Speech Recognition

Levinson, S. E., Rabiner, L. R., & Sondhi, M. M. (1983) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1983.tb03114.x · Cited by 978

Hidden Markov processes

Ephraim, Y., & Merhav, N. (2002) · IEEE Transactions on Information Theory · doi:10.1109/tit.2002.1003838 · Cited by 798

AN INTRODUCTION TO HIDDEN MARKOV MODELS AND BAYESIAN NETWORKS

GHAHRAMANI, Z. (2001) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001401000836 · Cited by 700

Untitled work

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From HMM's to segment models: a unified view of stochastic modeling for speech recognition

Ostendorf, M., Digalakis, V. V., & Kimball, O. A. (1996) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.536930 · Cited by 587

Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model

Duong, T. V., Bui, H. H., Phung, D. Q., & Venkatesh, S. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.61 · Cited by 535

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

A generalized hidden Markov model for the recognition of human genes in DNA.

Kulp, D., Haussler, D. H., Reese, M. G., & Eeckman, F. H. (1996) · PubMed · Cited by 386

A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology

Dong, M., & He, D. (2007) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2006.10.001 · Cited by 318

Video-based event recognition: activity representation and probabilistic recognition methods

Hongeng, S., Nevatia, R., & Bremond, F. (2004) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2004.02.005 · Cited by 287

Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis

Dong, M., & He, D. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.01.041 · Cited by 267

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

Yu, S.-Z., & Kobayashi, H. (2003) · IEEE Signal Processing Letters · doi:10.1109/lsp.2002.806705 · Cited by 226

Estimating Hidden Semi-Markov Chains From Discrete Sequences

Guédon, Y. (2003) · Journal of Computational and Graphical Statistics · doi:10.1198/1061860032030 · Cited by 199

Explicit modelling of state occupancy in hidden Markov models for automatic speech recognition

Russell, M., & Moore, R. (1985) · ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1985.1168477 · Cited by 194

A stochastic segment model for phoneme-based continuous speech recognition

Mari Ostendorf, E., & Roukos, S. (1989) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/29.45533 · Cited by 179

Deformable Markov model templates for time-series pattern matching

Ge, X., & Smyth, P. (2000) · Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/347090.347109 · Cited by 178

Average-Voice-Based Speech Synthesis Using HSMM-Based Speaker Adaptation and Adaptive Training

YAMAGISHI, J., & KOBAYASHI, T. (2007) · IEICE Transactions on Information and Systems · doi:10.1093/ietisy/e90-d.2.533 · Cited by 170

Stylized facts of financial time series and hidden semi-Markov models

Bulla, J., & Bulla, I. (2006) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2006.07.021 · Cited by 168

Baum's forward-backward algorithm revisited

Devijver, P. A. (1985) · Pattern Recognition Letters · doi:10.1016/0167-8655(85)90023-6 · Cited by 164

Speech recognition using hidden Markov models with polynomial regression functions as nonstationary states

Deng, L., Aksmanovic, M., Sun, X., & Wu, C. (1994) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.326610 · Cited by 152

PM2.5 concentration prediction using hidden semi-Markov model-based times series data mining

Dong, M., Yang, D., Kuang, Y., He, D., Erdal, S., & Kenski, D. M. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.12.017 · Cited by 149

Bayesian Segmentation of Protein Secondary Structure

Schmidler, S. C., Liu, J. S., & Brutlag, D. L. (2000) · Journal of Computational Biology · doi:10.1089/10665270050081496 · Cited by 148

Coupled Hidden Semi Markov Models for Activity Recognition

Natarajan, P., & Nevatia, R. (2007) · 2007 IEEE Workshop on Motion and Video Computing (WMVC'07) · doi:10.1109/wmvc.2007.12 · Cited by 144

Hidden semi-Markov model based speech synthesis

Zen, H., Tokuda, K., Masuko, T., Kobayashi, T., & Kitamura, T. (2004) · Interspeech (USB) · doi:10.21437/interspeech.2004-460 · Cited by 133

A daily behavior enabled hidden Markov model for human behavior understanding

Chung, P.-C., & Liu, C.-D. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2007.10.022 · Cited by 130

Practical implementation of an efficient forward-backward algorithm for an explicit-duration hidden Markov model

Yu, S., & Kobayashi, H. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.872540 · Cited by 120

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

A frame-synchronous network search algorithm for connected word recognition

Lee, C.-H., & Rabiner, L. R. (1989) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/29.46547 · Cited by 116

Protein secondary structure prediction for a single-sequence using hidden semi-Markov models

Aydın, Z., Altunbasak, Y., & Borodovsky, M. Y. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-178 · Cited by 113

A segment model based approach to speech recognition

Lee, C.-H., Soong, F. K., & Juang, B.-H. (2003) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1988.196629 · Cited by 112

Large-scale event detection using semi-hidden Markov models

Hongeng, S., & Nevatia, R. (2003) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238661 · Cited by 109

A hidden semi-Markov model with missing data and multiple observation sequences for mobility tracking

Yu, S., & Kobayashi, H. (2003) · Signal Processing · doi:10.1016/s0165-1684(02)00378-x · Cited by 108

Voice conversion using duration-embedded bi-HMMs for expressive speech synthesis

Wu, C., Hsia, C.-C., Liu, T.-H., & Wang, J.-F. (2006) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2006.876112 · Cited by 90

Equipment health diagnosis and prognosis using hidden semi-Markov models

Dong, M., He, D., Banerjee, P., & Keller, J. A. (2005) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-005-0111-0 · Cited by 86

Phoneme classification using semicontinuous hidden Markov models

Huang, X. (1992) · IEEE Transactions on Signal Processing · doi:10.1109/78.134469 · Cited by 85

Probabilistic-trajectory segmental HMMs

Holmes, W. J., & Russell, M. J. (1999) · Computer Speech & Language · doi:10.1006/csla.1998.0048 · Cited by 83

Multisensor triplet Markov chains and theory of evidence

Pieczynski, W. (2006) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2006.05.001 · Cited by 80

Capacity and complexity of HMM duration modeling techniques

Johnson, M. T. (2005) · IEEE Signal Processing Letters · doi:10.1109/lsp.2005.845598 · Cited by 80

An MCMC sampling approach to estimation of nonstationary hidden Markov models

Djurić, P. M., & Chun, J.-H. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.995067 · Cited by 80

Variable duration hidden Markov model and morphological segmentation for handwritten word recognition

Chen, M.-Y., Kundu, A., & Srihari, S. N. (1995) · IEEE Transactions on Image Processing · doi:10.1109/83.477074 · Cited by 79

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 78

Experimental evaluation of duration modelling techniques for automatic speech recognition

Russell, M. J., & COOK, A. (1987) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1987.1169918 · Cited by 73

hsmm — An R package for analyzing hidden semi-Markov models

Bulla, J., Bulla, I., & Nenadić, O. (2008) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2008.08.025 · Cited by 71

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

A Novel Model for Detecting Application Layer DDoS Attacks

Xie, Y., & Yu, S. (2006) · International Multi-Symposiums on Computer and Computational Sciences · doi:10.1109/imsccs.2006.159 · Cited by 70

A segmental HMM for speech pattern modelling

Russell, M. J. (1993) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1993.319351 · Cited by 66

Interacting Markov Random Fields for Simultaneous Terrain Modeling and Obstacle Detection

Wellington, C., Courville, A. C., & Stentz, A. (2005) · Robotics: Science and Systems · doi:10.15607/rss.2005.i.001 · Cited by 64

Isolated-utterance speech recognition using hidden Markov models with bounded state durations

Gu, H., Tseng, C.-Y., & Lee, L.-S. (1991) · IEEE Transactions on Signal Processing · doi:10.1109/78.91145 · Cited by 64

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

State duration modelling in hidden Markov models

Vaseghi, S. V. (1995) · Signal Processing · doi:10.1016/0165-1684(94)00088-h · Cited by 63

Development of an acoustic-phonetic hidden Markov model for continuous speech recognition

Ljolje, A., & Levinson, S. (1991) · IEEE Transactions on Signal Processing · doi:10.1109/78.80762 · Cited by 61

Continuously variable duration hidden Markov models for speech analysis

Levinson, S. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1986.1168801 · Cited by 59

Unsupervised restoration of hidden nonstationary Markov chains using evidential priors

Lanchantin, P., & Pieczynski, W. (2005) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2005.851131 · Cited by 58

Segmental hidden Markov models

Gales, M. J. F., & Young, S. J. (1993) · 3rd European Conference on Speech Communication and Technology (Eurospeech 1993) · doi:10.21437/eurospeech.1993-354 · Cited by 53

Online, Real-time Tracking and Recognition of Human Actions

Natarajan, P., & Nevatia, R. (2008) · IEEE Workshop on Motion and Video Computing · doi:10.1109/wmvc.2008.4544064 · Cited by 52

Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden Semi-Markov Model

Duong, T. V., Phung, D., Bui, H., & Venkatesh, S. (2006) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2006.635 · Cited by 52

The theory of segmental hidden Markov models

Gales, M., & Young, S. J. (1993) · Cambridge University Engineering Department Publications Database · Cited by 52

Modeling duration in a hidden Markov model with the exponential family

Mitchell, C. D., & Jamieson, L. H. (1993) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1993.319304 · Cited by 49

Robust parametric modeling of durations in hidden Markov models

Burshtein, D. (1996) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.496221 · Cited by 44

Speaker-independent phonetic classification using hidden Markov models with mixtures of trend functions

Deng, L. F., & Aksmanovic, M. (1997) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.593305 · Cited by 44

A new hybrid algorithm for speech recognition based on HMM segmentation and learning vector quantization

Katagiri, S., & Lee, C. (1993) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.242488 · Cited by 43

Arabic Handwritten Word Recognition Using HMMs with Explicit State Duration

Benouareth, A., Ennaji, A., & Sellami, M. (2007) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2008/247354 · Cited by 42

MATRIX-ANALYTIC ANALYSIS OF A MAP/PH/1 QUEUE FITTED TO WEB SERVER DATA

RISKA, A., SQUILLANTE, M. S., YU, S.-Z., LIU, Z., & ZHANG, L. (2002) · Matrix-Analytic Methods · doi:10.1142/9789812777164_0018 · Cited by 38

Robust parametric modeling of durations in hidden Markov models

Burshtein, D. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1995.479656 · Cited by 37

Hidden semi-Markov model for anomaly detection

Tan, X., & Xi, H. (2008) · Applied Mathematics and Computation · doi:10.1016/j.amc.2008.05.028 · Cited by 34

Detecting Objects of Variable Shape Structure With Hidden State Shape Models

Wang, J., Athitsos, V., Sclaroff, S., & Betke, M. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1178 · Cited by 34

Unobtrusive Assessment of Mobility

Pavel, M., Hayes, T. L., Adami, A., Jimison, H. B., & Kaye, J. A. (2006) · Conference proceedings · doi:10.1109/iembs.2006.260301 · Cited by 33

Unsupervised learning and mapping of active brain functional MRI signals based on hidden semi-Markov event sequence models

Faisan, S., Thoraval, L., Armspach, J., Metz‐Lutz, M., & Heitz, F. (2005) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.841225 · Cited by 33

A novel approach to equipment health management based on auto-regressive hidden semi-Markov model (AR-HSMM)

Dong, M. (2008) · Science in China Series F Information Sciences · doi:10.1007/s11432-008-0111-4 · Cited by 32

HSMM-Based Model Adaptation Algorithms for Average-Voice-Based Speech Synthesis

Yamagishi, J., Ogata, K., Nakano, Y., Isogai, J., & Kobayashi, T. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2006.1659961 · Cited by 31

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

Unsupervised segmentation of hidden semi-Markov non-stationary chains

Lapuyade‐Lahorgue, J., & Wojciech Pieczynski, E. (2011) · Signal Processing · doi:10.1016/j.sigpro.2011.06.001 · Cited by 29

An HTTP Flooding Detection Method Based on Browser Behavior

Lu, W.-Z., & Yu, S. (2006) · International Conference on Computational Intelligence and Security (CIS) · doi:10.1109/iccias.2006.295444 · Cited by 28

Segmental Hidden Markov Models with Random Effects for Waveform Modeling

Kim, S. Y., & Smyth, P. (2006) · doi:10.5555/1248547.1248580 · Cited by 28

Bayesian learning of speech duration models

Chien, J., & Huang, C. (2003) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2003.818114 · Cited by 27

Semi-supervised learning of probabilistic models for ECG segmentation

Hughes, N. P., Roberts, S., & Tarassenko, L. (2005) · doi:10.1109/iembs.2004.1403187 · Cited by 27

Topic transition detection using hierarchical hidden Markov and semi-Markov models

Phung, D., Duong, T. V., Venkatesh, S., & Bui, H. (2005) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1101149.1101153 · Cited by 26

Offline and online identification of hidden semi-Markov models

Azimi, M., Nasiopoulos, P., & Ward, R. K. (2005) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2005.850344 · Cited by 26

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

Efficient Hidden Semi-Markov Model Inference for Structured Video Sequences

Tweed, D., Fisher, R. B., Bins, J., & List, T. (2006) · IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS) · doi:10.1109/vspets.2005.1570922 · Cited by 24

Weighted Viterbi algorithm and state duration modelling for speech recognition in noise

Yoma, N. B., McInnes, F. R., & Jack, M. A. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1998.675363 · Cited by 24

Hierarchical multi-channel hidden semi Markov models

Natarajan, P., & Nevatia, R. (2007) · Cited by 23

Duration modeling with expanded HMM applied to speech recognition

Bonafonte, A., García-Vidal, J., & Rodríguez, A. N. (1996) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.1996-288 · Cited by 22

Noise robust speech recognition with state duration constraints

Laurila, K. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1997.596074 · Cited by 21

An efficient algorithm to find the best state sequence in HSMM

Bonafonte, A., Ros, X., & Marifio, J. B. (1993) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1993-346 · Cited by 21

A style control technique for speech synthesis using multiple regression HSMM

Nose, T., Yamagishi, J., & Kobayashi, T. (2006) · Interspeech (USB) · doi:10.21437/interspeech.2006-388 · Cited by 19

Adaptive Training for Hidden Semi-Markov Model

Yamagishi, J., & Kobayashi, T. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2005.1415126 · Cited by 19

An Adaptive Cusum Test Based on a Hidden Semi-Markov Model for Change Detection in Non-invasive Mean Blood Pressure Trend

Yang, P., Dumont, G. A., & Ansermino, J. M. (2006) · Conference proceedings · doi:10.1109/iembs.2006.4398176 · Cited by 17

Human walking motion synthesis based on multiple regression hidden semi-Markov model

Yamazaki, T., Niwase, N., Yamagishi, J., et al. (2005) · International Conference on Cyberworlds (CW) · doi:10.1109/cw.2005.51 · Cited by 16

Real-Time Video Intelligent Surveillance System

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On including temporal constraints in Viterbi alignment for speech recognition in noise

Yoma, N. B., McInnes, F. R., Jack, M. A., Stump, S. M. D., & Ling, L. L. (2001) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.902285 · Cited by 15

A Dynamic Anomaly Detection Model for Web User Behavior Based on HsMM

Xie, Y., & Yu, S. (2006) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd.2006.253054 · Cited by 14

Experimental evaluation of segmental HMMs

Holmes, W. J., & Russell, M. J. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1995.479647 · Cited by 14

Hidden Semi-Markov Model Based Speech Recognition System using Weighted Finite-State Transducer

Oura, K., Zen, H., Nankaku, Y., Lee, A., & Tokuda, K. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2006.1659950 · Cited by 13

A segmental-feature HMM for speech pattern modeling

Yun, Y., & Oh, Y. (2000) · IEEE Signal Processing Letters · doi:10.1109/97.844631 · Cited by 13

Modeling acoustic transitions in speech by modified hidden Markov models with state duration and state duration-dependent observation probabilities

Park, Y. K., Un, C. K., & Kwon, O. (1996) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.536933 · Cited by 11

Hmm-based semantic learning for a mobile robot

Levinson, S. E., & Squire, K. M. (2004) · Cited by 10

Use of semi-Markov models for speaker-independent phoneme recognition

Ratnayake, N. H., Savic, M. I., & Sorensen, J. (1992) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1992.225845 · Cited by 10

MAP speaker adaptation of state duration distributions for speech recognition

Yoma, N. B., & Sánchez, J. (2002) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2002.803441 · Cited by 10

Efficient Coxian Duration Modelling for Activity Recognition in Smart Environments with the Hidden semi-Markov Model

Duong, T. V., Phung, D., Bui, H., & Venkatesh, S. (2005) · International Conference on Intelligent Sensors, Sensor Networks and Information Processing · doi:10.1109/issnip.2005.1595592 · Cited by 10

Integration of context-dependent durational knowledge into HMM-based speech recognition

Wang, X., ten Bosch, L., & Pols, L. C. W. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.1109/icslp.1996.607791 · Cited by 9

A Network-wide Traffic Anomaly Detection Method Based on HSMM

Li, M., & Yu, S. (2006) · International Conference on Communications, Circuits and Systems · doi:10.1109/icccas.2006.284987 · Cited by 9

A complement to variable duration hidden Markov model in handwritten word recognition

Chen, M.-Y., & Kundu, A. (2002) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.1994.413298 · Cited by 9

Recursive Maximum Likelihood Estimation for Hidden Semi-Markov Models

Squire, K. M., & Levinson, S. E. (2006) · Machine learning for signal processing ... · doi:10.1109/mlsp.2005.1532923 · Cited by 9

Online identification of hidden Semi-Markov models

Azimi, M., Nasiopoulos, P., & Ward, R. K. (2004) · International symposium on image and signal processing and analysis/ISPA ... · doi:10.1109/ispa.2003.1296424 · Cited by 9

A unified way in incorporating segmental feature and segmental model into HMM

He, J. Y. D., & Leich, H. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1995.479646 · Cited by 9

Use of the Derin's algorithm in hidden semi-Markov models for automatic speech recognition

Guédon, Y., & Cocozza-Thivent, C. (2003) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1989.266420 · Cited by 8

Modeling non stationary hidden semi-markov chains with triplet markov chains and theory of evidence

Pieczynski, W. (2005) · IEEE/SP Workshop on Statistical Signal Processing (SSP) · doi:10.1109/ssp.2005.1628689 · Cited by 8

Reducing computational load in segmental hidden Markov model decoding for speech recognition

Russell, M. J. (2005) · Electronics Letters · doi:10.1049/el:20053420 · Cited by 7

Duration modelling in finite state automata for speech recognition and fast speaker adaptation

Codogno, M., & Fissore, L. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1987.1169481 · Cited by 6

HMM with global path constraint in Viterbi decoding for isolated word recognition

Kim, W.-G., Choi, J.-Y., & Youn, D. H. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1994.389221 · Cited by 5

Bayesian duration modeling and learning for speech recognition

Chien, J., & Huang, C. (2004) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2004.1326158 · Cited by 5

Multiple tracking based anomaly detection of mobile nodes

Yu, S. (2005) · International Conference on Mobile Technology, Applications, and Systems · doi:10.1109/mtas.2005.243755 · Cited by 5

Prediction of Protein Interdomain Linker Regions by a Nonstationary Hidden Markov Model

Bae, K., Mallick, B. K., & Elsik, C. G. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000661 · Cited by 4

A new signal model and identification algorithm for hidden semi-Markov signals

Azimi, M., Nasiopoulos, P., & Ward, R. K. (2004) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2004.1326309 · Cited by 4

Learning Human Activity Containing Sparse Irrelevant Events in Long Sequence

Zhang, W., Chen, F., Xu, W., & Du, Y. (2008) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2008.283 · Cited by 4

Unsupervised and semi-supervised training methods for eukaryotic gene prediction

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Clustering Web Traffic of Request Bursts

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