Journal articles: 'Hidden markov model training'

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Tarnas, C., and R. Hughey. "Reduced space hidden Markov model training." Bioinformatics 14, no. 5 (June 1, 1998): 401–6. http://dx.doi.org/10.1093/bioinformatics/14.5.401.

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Yi, Jie. "Method and apparatus for training hidden Markov model." Journal of the Acoustical Society of America 107, no. 5 (2000): 2324. http://dx.doi.org/10.1121/1.428600.

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Grewal, Jasleen K., Martin Krzywinski, and Naomi Altman. "Markov models — training and evaluation of hidden Markov models." Nature Methods 17, no. 2 (February 2020): 121–22. http://dx.doi.org/10.1038/s41592-019-0702-6.

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Kwong, S., Q. H. He, and K. F. Man. "Training approach for hidden Markov models." Electronics Letters 32, no. 17 (1996): 1554. http://dx.doi.org/10.1049/el:19961080.

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Yulin, Sergey S., and Irina N. Palamar. "Probability Model Based on Cluster Analysis to Classify Sequences of Observations for Small Training Sets." Statistics, Optimization & Information Computing 8, no. 1 (February 18, 2020): 296–303. http://dx.doi.org/10.19139/soic-2310-5070-690.

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The problem of recognizing patterns, when there are few training data available, is particularly relevant and arises in cases when collection of training data is expensive or essentially impossible. The work proposes a new probability model MC&CL (Markov Chain and Clusters) based on a combination of markov chain and algorithm of clustering (self-organizing map of Kohonen, k-means method), to solve a problem of classifying sequences of observations, when the amount of training dataset is low. An original experimental comparison is made between the developed model (MC&CL) and a number of the other popular models to classify sequences: HMM (Hidden Markov Model), HCRF (Hidden Conditional Random Fields),LSTM (Long Short-Term Memory), kNN+DTW (k-Nearest Neighbors algorithm + Dynamic Time Warping algorithm). A comparison is made using synthetic random sequences, generated from the hidden markov model, with noise added to training specimens. The best accuracy of classifying the suggested model is shown, as compared to those under review, when the amount of training data is low.

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Kan-Wing Mak, B., and E. Bocchieri. "Direct training of subspace distribution clustering hidden Markov model." IEEE Transactions on Speech and Audio Processing 9, no. 4 (May 2001): 378–87. http://dx.doi.org/10.1109/89.917683.

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Yuan, Shenfang, Jinjin Zhang, Jian Chen, Lei Qiu, and Weibo Yang. "A uniform initialization Gaussian mixture model–based guided wave–hidden Markov model with stable damage evaluation performance." Structural Health Monitoring 18, no. 3 (June 29, 2018): 853–68. http://dx.doi.org/10.1177/1475921718783652.

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During practical applications, the time-varying service conditions usually lead to difficulties in properly interpreting structural health monitoring signals. The guided wave–hidden Markov model–based damage evaluation method is a promising approach to address the uncertainties caused by the time-varying service condition. However, researches that have been performed to date are not comprehensive. Most of these research studies did not introduce serious time-varying factors, such as those that exist in reality, and hidden Markov model was applied directly without deep consideration of the performance improvement of hidden Markov model itself. In this article, the training stability problem when constructing the guided wave–hidden Markov model initialized by usually adopted k-means clustering method and its influence to damage evaluation were researched first by applying it to fatigue crack propagation evaluation of an attachment lug. After illustrating the problem of stability induced by k-means clustering, a novel uniform initialization Gaussian mixture model–based guided wave–hidden Markov model was proposed that provides steady and reliable construction of the guided wave–hidden Markov model. The advantage of the proposed method is demonstrated by lug fatigue crack propagation evaluation experiments.

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Gales, M. J. F. "Cluster adaptive training of hidden Markov models." IEEE Transactions on Speech and Audio Processing 8, no. 4 (July 2000): 417–28. http://dx.doi.org/10.1109/89.848223.

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Rabiner, Lawrence R., Chin‐Hui Lee, Biing‐Hwang Juang, David B. Roe, and Jay G. Wilpon. "Improved training procedures for hidden Markov models." Journal of the Acoustical Society of America 84, S1 (November 1988): S61. http://dx.doi.org/10.1121/1.2026404.

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Narwal, Priti, Deepak Kumar, and Shailendra N. Singh. "A Hidden Markov Model Combined With Markov Games for Intrusion Detection in Cloud." Journal of Cases on Information Technology 21, no. 4 (October 2019): 14–26. http://dx.doi.org/10.4018/jcit.2019100102.

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Cloud computing has evolved as a new paradigm for management of an infrastructure and gained ample consideration in both industrial and academic area of research. A hidden Markov model (HMM) combined with Markov games can give a solution that may act as a countermeasure for many cyber security threats and malicious intrusions in a network or in a cloud. A HMM can be trained by using training sequences that may be obtained by analyzing the file traces of packet analyzer like Wireshark network analyzer. In this article, the authors have proposed a model in which HMM can be build using a set of training examples that are obtained by using a network analyzer (i.e., Wireshark). As it is not an intrusion detection system, the obtained file traces may be used as training examples to test a HMM model. It also predicts a probability value for each tested sequence and states if sequence is anomalous or not. A numerical example is also shown in this article that calculates the most optimal sequence of observations for both HMM and state sequence probabilities in case a HMM model is already given.

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Pepper, D. J., T. P. Barnwell, and M. A. Clements. "Using a ring parallel processor for hidden Markov model training." IEEE Transactions on Acoustics, Speech, and Signal Processing 38, no. 2 (1990): 366–69. http://dx.doi.org/10.1109/29.103076.

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Wang, Litao, Mostafa G. Mehrabi, and Elijah Kannatey-Asibu,. "Hidden Markov Model-based Tool Wear Monitoring in Turning." Journal of Manufacturing Science and Engineering 124, no. 3 (July 11, 2002): 651–58. http://dx.doi.org/10.1115/1.1475320.

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This paper presents a new modeling framework for tool wear monitoring in machining processes using hidden Markov models (HMMs). Feature vectors are extracted from vibration signals measured during turning. A codebook is designed and used for vector quantization to convert the feature vectors into a symbol sequence for the hidden Markov model. A series of experiments are conducted to evaluate the effectiveness of the approach for different lengths of training data and observation sequence. Experimental results show that successful tool state detection rates as high as 97% can be achieved by using this approach.

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Lember, Jüri, and Alexey Koloydenko. "The adjusted Viterbi training for hidden Markov models." Bernoulli 14, no. 1 (February 2008): 180–206. http://dx.doi.org/10.3150/07-bej105.

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Ben-Yishai, A., and D. Burshtein. "A Discriminative Training Algorithm for Hidden Markov Models." IEEE Transactions on Speech and Audio Processing 12, no. 3 (May 2004): 204–17. http://dx.doi.org/10.1109/tsa.2003.822639.

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Ueda, Nobuhisa, and Taisuke Sato. "Simplified training algorithm for hierarchical hidden Markov models." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 87, no. 5 (2004): 59–69. http://dx.doi.org/10.1002/ecjc.10172.

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Dong, Lei, Jianfei Wang, Ming-Lang Tseng, Zhiyong Yang, Benfu Ma, and Ling-Ling Li. "Gyro Motor State Evaluation and Prediction Using the Extended Hidden Markov Model." Symmetry 12, no. 11 (October 22, 2020): 1750. http://dx.doi.org/10.3390/sym12111750.

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This study extracted the featured vectors in the same way from testing data and substituted these vectors into a trained hidden Markov model to get the log likelihood probability. The log likelihood probability was matched with the time–probability curve from where the gyro motor state evaluation and prediction were realized. A core component of gyroscopes is linked to the reliability of the inertia system to conduct gyro motor state evaluation and prediction. This study features the vectors’ extraction from full life cycle gyro motor data and completes the training model to feature the vectors according to the time sequence and extraction to full life cycle data undergoing hidden Markov model training. This proposed model applies to full life cycle gyro motor data for validation, compared with traditional hidden Markov model predictive methods and health condition-trained data. The results suggest precise evaluation and prediction and provide an important basis for gyro motor repair and replacement strategies.

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Yao, Zhe He, Xin Li, and Zi Chen Chen. "Prediction of Cutting Chatter Based on Hidden Markov Model." Key Engineering Materials 353-358 (September 2007): 2712–15. http://dx.doi.org/10.4028/www.scientific.net/kem.353-358.2712.

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Self-chatter is a serious problem in cutting process. This paper aims to solve the problem by establishing time series model of vibration acceleration signal in cutting process based on Hidden Markov Model (HMM) technology and achieve the purpose of chatter recognition and prediction. Features which can indicate cutting state are extracted from the acceleration signal. HMM parameters are obtained by model training, and the reference models database is built. Then cutting state recognition is performed according to the feature matching level. Simulations and experiments are conducted, and the results show that the proposed method is feasible and it could get high recognition

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Tavanaei, Amirhossein, and Anthony S. Maida. "Training a Hidden Markov Model with a Bayesian Spiking Neural Network." Journal of Signal Processing Systems 90, no. 2 (June 27, 2016): 211–20. http://dx.doi.org/10.1007/s11265-016-1153-2.

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Bărbulescu, Adrian, and Daniel I. Morariu. "Part of Speech Tagging Using Hidden Markov Models." International Journal of Advanced Statistics and IT&C for Economics and Life Sciences 10, no. 1 (December 1, 2020): 31–42. http://dx.doi.org/10.2478/ijasitels-2020-0005.

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Abstract In this paper, we present a wide range of models based on less adaptive and adaptive approaches for a PoS tagging system. These parameters for the adaptive approach are based on the n-gram of the Hidden Markov Model, evaluated for bigram and trigram, and based on three different types of decoding method, in this case forward, backward, and bidirectional. We used the Brown Corpus for the training and the testing phase. The bidirectional trigram model almost reaches state of the art accuracy but is disadvantaged by the decoding speed time while the backward trigram reaches almost the same results with a way better decoding speed time. By these results, we can conclude that the decoding procedure it’s way better when it evaluates the sentence from the last word to the first word and although the backward trigram model is very good, we still recommend the bidirectional trigram model when we want good precision on real data.

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He, Binjie, Dong Zhang, and Chang Zhao. "Hidden Markov Model-based Load Balancing in Data Center Networks." Computer Journal 63, no. 10 (December 7, 2019): 1449–62. http://dx.doi.org/10.1093/comjnl/bxz142.

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Abstract Modern data centers provide multiple parallel paths for end-to-end communications. Recent studies have been done on how to allocate rational paths for data flows to increase the throughput of data center networks. A centralized load balancing algorithm can improve the rationality of the path selection by using path bandwidth information. However, to ensure the accuracy of the information, current centralized load balancing algorithms monitor all the link bandwidth information in the path to determine the path bandwidth. Due to the excessive link bandwidth information monitored by the controller, however, much time is consumed, which is unacceptable for modern data centers. This paper proposes an algorithm called hidden Markov Model-based Load Balancing (HMMLB). HMMLB utilizes the hidden Markov Model (HMM) to select paths for data flows with fewer monitored links, less time cost, and approximate the same network throughput rate as a traditional centralized load balancing algorithm. To generate HMMLB, this research first turns the problem of path selection into an HMM problem. Secondly, deploying traditional centralized load balancing algorithms in the data center topology to collect training data. Finally, training the HMM with the collected data. Through simulation experiments, this paper verifies HMMLB’s effectiveness.

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Du, Shi Ping, Jian Wang, and Yu Ming Wei. "The Training Algorithm of Fuzzy Coupled Hidden Markov Models." Applied Mechanics and Materials 568-570 (June 2014): 254–59. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.254.

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A variety of coupled hidden Markov models (CHMMs) have recently been proposed as extensions of HMM to better characterize multiple interdependent sequences. The resulting models have multiple state variables that are temporally coupled via matrices of conditional probabilities. A generalised fuzzy approach to statistical modelling techniques is proposed in this paper. Fuzzy C-means (FCM) and fuzzy entropy (FE) techniques are combined into a generalised fuzzy technique and applied to coupled hidden Markov models. The CHMM based on the fuzzy c-means (FCM) and fuzzy entropy (FE) is referred to as FCM-FE-CHMM in this paper. By building up a generalised fuzzy objective function, several new formulae solving Training algorithms are theoretically derived for FCM-FE-CHMM. The fuzzy modelling techniques are very flexible since the degree of fuzziness, the degree of fuzzy entropy.

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Davis Bundi, Ntwiga,, and Weke Patrick. "Credit Scoring for M-Shwari using Hidden Markov Model." European Scientific Journal, ESJ 12, no. 15 (May 30, 2016): 176. http://dx.doi.org/10.19044/esj.2016.v12n15p176.

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The introduction of mobile based Micro-credit facility, M-Shwari, has heightened the need to develop a proper decision support system to classify the customers based on their credit scores. This arises due to lack of proper information on the poor and unbanked as they are locked out of the formal banking sector. A classification technique, the hidden Markov model, is used. The poor customers’ scanty deposits and withdrawal dynamics in the M-Shwari account estimate the credit risk factors that are used in training and learning the hidden Markov model. The data is generated through simulation and customers categorized in terms of their credit scores and credit quality levels. The model classifies over 80 percent of the customers as having average and good credit quality level. This approach offers a simple and novice method to cater for the unbanked and poor with minimal or no financial history thus increasing financial inclusion in Kenya.

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Xu, Jiawei, and Qian Luo. "Human action recognition based on mixed gaussian hidden markov model." MATEC Web of Conferences 336 (2021): 06004. http://dx.doi.org/10.1051/matecconf/202133606004.

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Human action recognition is a challenging field in recent years. Many traditional signal processing and machine learning methods are gradually trying to be applied in this field. This paper uses a hidden Markov model based on mixed Gaussian to solve the problem of human action recognition. The model treats the observed human actions as samples which conform to the Gaussian mixture model, and each Gaussian mixture model is determined by a state variable. The training of the model is the process that obtain the model parameters through the expectation maximization algorithm. The simulation results show that the Hidden Markov Model based on the mixed Gaussian distribution can perform well in human action recognition.

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Kim, H. R., and H. S. Lee. "Segmental corrective training for hidden Markov model parameter estimation in speech recognition." Electronics Letters 27, no. 18 (1991): 1633. http://dx.doi.org/10.1049/el:19911021.

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Liu, Hui, Wei Wang, and Chuang Wen Wang. "A Novel Research in Low Altitude Acoustic Target Recognition Based on HMM." International Journal of Multimedia Data Engineering and Management 12, no. 2 (April 2021): 19–30. http://dx.doi.org/10.4018/ijmdem.2021040102.

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This paper introduces an improved HMM (hidden Markov model) for low altitude acoustic target recognition. To overcome the limitation of the classical CDHMM (continuous density hidden Markov model) training algorithm and the generalization ability deficiency of existing discriminative learning methods, a new discriminative training method for estimating the CDHMM in acoustic target recognition is proposed based on the principle of maximizing the minimum relative separation margin. According to the definition of the relative margin, the new training criterion can be equation as a standard constrained minimax optimization problem. Then, the optimization problem can be solved by a GPD (generalized probabilistic descent) algorithm. The experimental results show that the performance of the algorithm is significantly improved compared with the former training method, which can effectively improve the recognition ability of the acoustic target recognition system.

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Chatzis, Sotirios, Dimitrios Kosmopoulos, and George Papadourakis. "A Nonstationary Hidden Markov Model with Approximately Infinitely-Long Time-Dependencies." International Journal on Artificial Intelligence Tools 25, no. 05 (September 15, 2016): 1640001. http://dx.doi.org/10.1142/s0218213016400017.

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Hidden Markov models (HMMs) are a popular approach for modeling sequential data, typically based on the assumption of a first-order Markov chain. In other words, only one-step back dependencies are modeled which is a rather unrealistic assumption in most applications. In this paper, we propose a method for postulating HMMs with approximately infinitely-long time-dependencies. Our approach considers the whole history of model states in the postulated dependencies, by making use of a recently proposed nonparametric Bayesian method for modeling label sequences with infinitely-long time dependencies, namely the sequence memoizer. We manage to derive training and inference algorithms for our model with computational costs identical to simple first-order HMMs, despite its entailed infinitely-long time-dependencies, by employing a mean-field-like approximation. The efficacy of our proposed model is experimentally demonstrated.

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He, Xiaodong, and Li Deng. "A new look at discriminative training for hidden Markov models." Pattern Recognition Letters 28, no. 11 (August 2007): 1285–94. http://dx.doi.org/10.1016/j.patrec.2006.11.022.

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Luginbuhl, Tod E. "Training of homoscedastic hidden Markov models for automatic speech recognition." Journal of the Acoustical Society of America 100, no. 4 (1996): 1943. http://dx.doi.org/10.1121/1.417894.

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Xiaolin Li, M. Parizeau, and R. Plamondon. "Training hidden Markov models with multiple observations-a combinatorial method." IEEE Transactions on Pattern Analysis and Machine Intelligence 22, no. 4 (April 2000): 371–77. http://dx.doi.org/10.1109/34.845379.

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Moghaddam, Zia, and Massimo Piccardi. "Training Initialization of Hidden Markov Models in Human Action Recognition." IEEE Transactions on Automation Science and Engineering 11, no. 2 (April 2014): 394–408. http://dx.doi.org/10.1109/tase.2013.2262940.

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Aupetit, Sébastien, Nicolas Monmarché, and Mohamed Slimane. "Hidden Markov Models Training by a Particle Swarm Optimization Algorithm." Journal of Mathematical Modelling and Algorithms 6, no. 2 (February 28, 2006): 175–93. http://dx.doi.org/10.1007/s10852-005-9037-7.

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PARK, HEE-SEON, BONG-KEE SIN, JONGSUB MOON, and SEONG-WHAN LEE. "A 2-D HMM METHOD FOR OFFLINE HANDWRITTEN CHARACTER RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 01 (February 2001): 91–105. http://dx.doi.org/10.1142/s0218001401000757.

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In this paper we consider a hidden Markov mesh random field (HMMRF) for character recognition. The model consists of a "hidden" Markov mesh random field (MMRF) and an overlying probabilistic observation function of the MMRF. Just like the 1-D HMM, the hidden layer is characterized by the initial and the transition probability distributions, and the observation layer is defined by distribution functions for vector-quantized (VQ) observations. The HMMRF-based method consists of two phases: decoding and training. The decoding and the training algorithms are developed using dynamic programming and maximum likelihood estimation methods. To accelerate the computation in both phases, we employed a look-ahead scheme based on maximum marginal it a posteriori probability criterion for third-order HMMRF. Tested on a larget-set handwritten Korean Hangul character database, the model showed a promising result: up to 87.2% recognition rate with 8 state HMMRF and 128 VQ levels.

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Shiping Du, Jian Wang, and Yuming Wei. "A Training Algorithms of Third-order Hidden Markov Model based on FCM-FE." International Journal of Advancements in Computing Technology 3, no. 7 (August 31, 2011): 160–68. http://dx.doi.org/10.4156/ijact.vol3.issue7.21.

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Cidota, Marina A., and Monica Dumitrescu. "A Multinomial Hidden Markov Model and its training by a combined iterative procedure." AI Communications 27, no. 2 (2014): 143–55. http://dx.doi.org/10.3233/aic-130589.

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Xiaobin Li, Jiansheng Qian, and Zhikai Zhao. "Discrete Hidden Markov Model Training Based on Variable Length Particle Swarm Optimization Algorithm." International Journal of Digital Content Technology and its Applications 6, no. 20 (November 30, 2012): 182–91. http://dx.doi.org/10.4156/jdcta.vol6.issue20.20.

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Li, Hui, Xinyu Zhang, Ran Cui, and Na Wang. "Education of Recognition Training Combined with Hidden Markov Model to Explore English Speaking." Computer-Aided Design and Applications 17, S1 (July 25, 2019): 101–12. http://dx.doi.org/10.14733/cadaps.2020.s1.101-112.

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Xie, Fengyun, Bo Wu, Youmin Hu, Yan Wang, Guangfei Jia, and Yao Cheng. "A generalized interval probability-based optimization method for training generalized hidden Markov model." Signal Processing 94 (January 2014): 319–29. http://dx.doi.org/10.1016/j.sigpro.2013.06.009.

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Matassoni, Marco, Maurizio Omologo, Diego Giuliani, and Piergiorgio Svaizer. "Hidden Markov model training with contaminated speech material for distant-talking speech recognition." Computer Speech & Language 16, no. 2 (April 2002): 205–23. http://dx.doi.org/10.1006/csla.2002.0191.

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Jaeger, Herbert. "Observable Operator Models for Discrete Stochastic Time Series." Neural Computation 12, no. 6 (June 1, 2000): 1371–98. http://dx.doi.org/10.1162/089976600300015411.

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A widely used class of models for stochastic systems is hidden Markov models. Systems that can be modeled by hidden Markov models are a proper subclass of linearly dependent processes, a class of stochastic systems known from mathematical investigations carried out over the past four decades. This article provides a novel, simple characterization of linearly dependent processes, called observable operator models. The mathematical properties of observable operator models lead to a constructive learning algorithm for the identification of linearly dependent processes. The core of the algorithm has a time complexity of O (N + nm3), where N is the size of training data, n is the number of distinguishable outcomes of observations, and m is model state-space dimension.

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Du, Shi Ping, Jian Wang, and Yu Ming Wei. "The Learning Algorithms of Coupled Discrete Hidden Markov Models." Applied Mechanics and Materials 411-414 (September 2013): 2106–10. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.2106.

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A hidden Markov model (HMM) encompasses a large class of stochastic process models and has been successfully applied to a number of scientific and engineering problems, including speech and other pattern recognition problems, and biological sequence analysis. A major restriction is found, however, in conventional HMM, i.e., it is ill-suited to capture the interactions among different models. A variety of coupled hidden Markov models (CHMMs) have recently been proposed as extensions of HMM to better characterize multiple interdependent sequences. The resulting models have multiple state variables that are temporally coupled via matrices of conditional probabilities. This paper study is focused on the coupled discrete HMM, there are two state variables in the network. By generalizing forward-backward algorithm, Viterbi algorithm and Baum-Welch algorithm commonly used in conventional HMM to accommodate two state variables, several new formulae solving the 2-chain coupled discrete HMM probability evaluation, decoding and training problem are theoretically derived.

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Bezoui, Mouaz. "Speech Recognition of Moroccan Dialect Using Hidden Markov Models." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 1 (March 1, 2019): 7. http://dx.doi.org/10.11591/ijai.v8.i1.pp7-13.

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<p>This paper addresses the development of an Automatic Speech Recognition (ASR) system for the Moroccan Dialect. Dialectal Arabic (DA) refers to the day-to-day vernaculars spoken in the Arab world. In fact, Moroccan Dialect is very different from the Modern Standard Arabic (MSA) because it is highly influenced by the French Language. It is observed throughout all Arab countries that standard Arabic widely written and used for official speech, news papers, public administration and school but not used in everyday conversation and dialect is widely spoken in everyday life but almost never written. we propose to use the Mel Frequency Cepstral Coefficient (MFCC) features to specify the best speaker identification system. The extracted speech features are quantized to a number of centroids using vector quantization algorithm. These centroids constitute the codebook of that speaker. MFCC’s are calculated in training phase and again in testing phase. Speakers uttered same words once in a training session and once in a testing session later. The Euclidean distance between the MFCC’s of each speaker in training phase to the centroids of individual speaker in testing phase is measured and the speaker is identified according to the minimum Euclidean distance. The code is developed in the MATLAB environment and performs the identification satisfactorily.</p>

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Selvaraj, Lokesh, and Balakrishnan Ganesan. "Enhancing Speech Recognition Using Improved Particle Swarm Optimization Based Hidden Markov Model." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/270576.

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Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested. The suggested methodology contains four stages, namely, (i) denoising, (ii) feature mining (iii), vector quantization, and (iv) IPSO based hidden Markov model (HMM) technique (IP-HMM). At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC), mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy.

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Young, S. J. "Competitive training: a connectionist approach to the discriminative training of hidden Markov models." IEE Proceedings I Communications, Speech and Vision 138, no. 1 (1991): 61. http://dx.doi.org/10.1049/ip-i-2.1991.0008.

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Ye, Ning, Zhong-qin Wang, Reza Malekian, Qiaomin Lin, and Ru-chuan Wang. "A Method for Driving Route Predictions Based on Hidden Markov Model." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/824532.

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We present a driving route prediction method that is based on Hidden Markov Model (HMM). This method can accurately predict a vehicle’s entire route as early in a trip’s lifetime as possible without inputting origins and destinations beforehand. Firstly, we propose the route recommendation system architecture, where route predictions play important role in the system. Secondly, we define a road network model, normalize each of driving routes in the rectangular coordinate system, and build the HMM to make preparation for route predictions using a method of training set extension based onK-means++ and the add-one (Laplace) smoothing technique. Thirdly, we present the route prediction algorithm. Finally, the experimental results of the effectiveness of the route predictions that is based on HMM are shown.

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Osman, Mahmoud Ali, Nasser Ali, and S. A. Elfandi. "Isolated Words Digits Speech Recognition." Advanced Materials Research 433-440 (January 2012): 4983–88. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4983.

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This paper implemented a speech recognition program for isolated digit words using a method called the Hidden Markov Model (HMM) for speech modeling. The K-means,Baun-welch algorithms for training and codebook conception and finally the Viterbi decoding algorithm for recognition process. This method uses a statistical approach in characterizing speech. Briefly, speech utterance is fit into a probabilistic framework, which consists of transition of states and observable sequences. The target is to evaluate the probability score of the speech utterance based on a given model, and also to find the best model that gives the highest probability score. Research has shown that the HMM method is superior over conventional template matching methods, and it has already been applied by oversea companies successfully in commercial speech recognition programs. Implementing a LP Cepstrum, Coefficient function, a training function, which creates Hidden Markov Models of specific utterances and a testing function, testing utterances on the models created by the training-function. These functions created in MatLab. The recognized word decision is based on the maximal likehood value. The speech database is TI46 which is downloading from internet.

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Azari, David P., Yu Hen Hu, Brady L. Miller, Brian V. Le, and Robert G. Radwin. "Using Surgeon Hand Motions to Predict Surgical Maneuvers." Human Factors: The Journal of the Human Factors and Ergonomics Society 61, no. 8 (April 23, 2019): 1326–39. http://dx.doi.org/10.1177/0018720819838901.

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Objective: This study explores how common machine learning techniques can predict surgical maneuvers from a continuous video record of surgical benchtop simulations. Background: Automatic computer vision recognition of surgical maneuvers (suturing, tying, and transition) could expedite video review and objective assessment of surgeries. Method: We recorded hand movements of 37 clinicians performing simple and running subcuticular suturing benchtop simulations, and applied three machine learning techniques (decision trees, random forests, and hidden Markov models) to classify surgical maneuvers every 2 s (60 frames) of video. Results: Random forest predictions of surgical video correctly classified 74% of all video segments into suturing, tying, and transition states for a randomly selected test set. Hidden Markov model adjustments improved the random forest predictions to 79% for simple interrupted suturing on a subset of randomly selected participants. Conclusion: Random forest predictions aided by hidden Markov modeling provided the best prediction of surgical maneuvers. Training of models across all users improved prediction accuracy by 10% compared with a random selection of participants. Application: Marker-less video hand tracking can predict surgical maneuvers from a continuous video record with similar accuracy as robot-assisted surgical platforms, and may enable more efficient video review of surgical procedures for training and coaching.

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Anraeni, Siska, Ingrid Nurtanio, and Indrabayu Indrabayu. "Detection of Kidney Organ Condition Using Hidden Markov Models." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (August 1, 2015): 294. http://dx.doi.org/10.11591/tijee.v15i2.1542.

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The frequencies of chronic kidney disease are likely to continue to increase worldwide. So people need to take a precaution, which is by maintaining kidney health and early detection of renal impairment by analyzing the composition of the iris is known as iridology. This paper presents a novel approach using a one-dimensional discrete Hidden Markov Model (HMM) classifier and coefficients Singular Value Decomposition (SVD) as a feature for image recognition iris to indicate normal or abnormal kidney. The system has been examined on 200 iris images. The total images of the abnormal kidney condition were 100 images and those for the normal kidney condition were 100 images. The system showed a classification rate up to 100% using total of image for training and testing the system unspecified, resize iris image 56x46 pixels, coefficient values U(1,1), Σ(1,1) and Σ(2,2), quantized values [18 10 7], and classify by 7-state HMM with .pgm format database.

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Arslan, L. M., and J. H. L. Hansen. "Selective training for hidden Markov models with applications to speech classification." IEEE Transactions on Speech and Audio Processing 7, no. 1 (1999): 46–54. http://dx.doi.org/10.1109/89.736330.

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Woodland, P. C., and D. Povey. "Large scale discriminative training of hidden Markov models for speech recognition." Computer Speech & Language 16, no. 1 (January 2002): 25–47. http://dx.doi.org/10.1006/csla.2001.0182.

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Ani, Tarik Al, and Yskandar Hamam. "A low complexity simulated annealing approach for training hidden Markov models." International Journal of Operational Research 8, no. 4 (2010): 483. http://dx.doi.org/10.1504/ijor.2010.034070.

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Journal articles on the topic 'Hidden markov model training'

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