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Relevant bibliographies by topics / Baum Welch training

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Academic literature on the topic 'Baum Welch training'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Baum Welch training"

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Lucke, H. "Which stochastic models allow Baum-Welch training?" IEEE Transactions on Signal Processing 44, no. 11 (1996): 2746–56. http://dx.doi.org/10.1109/78.542181.

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Pylkkonen, Janne, and Mikko Kurimo. "Analysis of Extended Baum–Welch and Constrained Optimization for Discriminative Training of HMMs." IEEE Transactions on Audio, Speech, and Language Processing 20, no. 9 (2012): 2409–19. http://dx.doi.org/10.1109/tasl.2012.2203805.

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Duan, Haonan, Abdullah Rashwan, Pascal Poupart, and Zhitang Chen. "Discriminative training of feed-forward and recurrent sum-product networks by extended Baum-Welch." International Journal of Approximate Reasoning 124 (September 2020): 66–81. http://dx.doi.org/10.1016/j.ijar.2020.02.007.

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Wu, Li Ming, Qi Li, and Si Cheng Chen. "Research of Multi-Touch Projection Camera Interaction Techniques Based on SOPC." Applied Mechanics and Materials 543-547 (March 2014): 784–87. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.784.

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Projection-camera interaction is a human-computer interaction technology which uses projector-camera as input-output devices, and combined with human movement as interactive mode. Introducting radial distortion and tangential distortion of lens to projection-geometric calibrate the camera system, the platform is used the Xilinx main processor Virtex-4 FX experimental box to building a projection-camera interactive implementation platform, and used the Baum-Welch algorithm which based on hidden Markov model for training gesture.

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Ocaña, M., L. M. Bergasa, M. A. Sotelo, R. Flores, D. F. Llorca, and D. Schleicher. "Automatic training method applied to a WiFi+ultrasound POMDP navigation system." Robotica 27, no. 7 (2009): 1049–61. http://dx.doi.org/10.1017/s0263574709005463.

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SUMMARYThis paper presents an automatic training method based on the Baum–Welch algorithm (also known as EM algorithm) and a robust low-level controller. The method has been applied to the indoor autonomous navigation of a surveillance robot that utilizes a WiFi+Ultrasound Partially Observable Markov Decision Process (POMDP). This method uses a robust local navigation system to automatically provide some WiFi+Ultrasound maps. These maps could be employed within probabilistic global robot localization systems. These systems use a priori probabilistic map in order to estimate the global robot po

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Long, Hai Xia, Li Hua Wu, and Yu Zhang. "Multiple Sequence Alignment Based on Profile Hidden Markov Model and Quantum-Behaved Particle Swarm Optimization with Selection Method." Advanced Materials Research 282-283 (July 2011): 7–12. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.7.

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Multiple sequence alignment (MSA) is an NP-complete and important problem in bioinformatics. Currently, profile hidden Markov model (HMM) is widely used for multiple sequence alignment. In this paper, Quantum-behaved Particle Swarm Optimization with selection operation (SQPSO) is presented, which is used to train profile HMM. Furthermore, an integration algorithm based on the profile HMM and SQPSO for the MSA is constructed. The approach is examined by using multiple nucleotides and protein sequences and compared with other algorithms. The results of the comparisons show that the HMM trained w

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Baggenstoss, Paul M. "System and method for training a class-specific hidden markov model using a modified baum-welch algorithm." Journal of the Acoustical Society of America 113, no. 4 (2003): 1793. http://dx.doi.org/10.1121/1.1572369.

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Holmes, Ian. "Using evolutionary Expectation Maximization to estimate indel rates." Bioinformatics 21, no. 10 (2005): 2294–300. http://dx.doi.org/10.1093/bioinformatics/bti177.

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Abstract Motivation The Expectation Maximization (EM) algorithm, in the form of the Baum–Welch algorithm (for hidden Markov models) or the Inside-Outside algorithm (for stochastic context-free grammars), is a powerful way to estimate the parameters of stochastic grammars for biological sequence analysis. To use this algorithm for multiple-sequence evolutionary modelling, it would be useful to apply the EM algorithm to estimate not only the probability parameters of the stochastic grammar, but also the instantaneous mutation rates of the underlying evolutionary model (to facilitate the developm

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Jiang, Zhiwei, Xiaoqing Ding, Liangrui Peng, and Changsong Liu. "Exploring More Representative States of Hidden Markov Model in Optical Character Recognition: A Clustering-Based Model Pre-Training Approach." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 03 (2015): 1550014. http://dx.doi.org/10.1142/s0218001415500147.

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Hidden Markov Model (HMM) is an effective method to describe sequential signals in many applications. As to model estimation issue, common training algorithm only focuses on the optimization of model parameters. However, model structure influences system performance as well. Although some structure optimization methods are proposed, they are usually implemented as an independent module before parameter optimization. In this paper, the clustering feature of states in HMM is discussed through comparing the mechanism of Quadratic Discriminant Function (QDF) classifier and HMM. Then, through the c

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Li, Qiang, Ming Bing Zhao, and Yong Feng. "Speaker Identification in Total Variability Space." Applied Mechanics and Materials 401-403 (September 2013): 1489–92. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1489.

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Gaussian Mixture Model-Universal Background Model based approaches have been popular used for speaker identification task. But in real complex environment the identification system performs too much worse than in laboratory, and the main reason is the mismatch of the training and testing channel and also the variability of the speaker himself. In this paper we introduce i-vector to the speaker identification system. In i-vector approach, a low dimensional subspace called total variability space is used to estimate both speaker and channel variability. Baum-Welch statistics are first computed o

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Dissertations / Theses on the topic "Baum Welch training"

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Majewsky, Stefan. "Training of Hidden Markov models as an instance of the expectation maximization algorithm." Bachelor's thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-226903.

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In Natural Language Processing (NLP), speech and text are parsed and generated with language models and parser models, and translated with translation models. Each model contains a set of numerical parameters which are found by applying a suitable training algorithm to a set of training data. Many such training algorithms are instances of the Expectation-Maximization (EM) algorithm. In [BSV15], a generic EM algorithm for NLP is described. This work presents a particular speech model, the Hidden Markov model, and its standard training algorithm, the Baum-Welch algorithm. It is then shown that

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Wilhelmsson, Anna, and Sofia Bedoire. "Driving Behavior Prediction by Training a Hidden Markov Model." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291656.

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Introducing automated vehicles in to traffic withhuman drivers, human behavior prediction is essential to obtainoperation safety. In this study, a human behavior estimationmodel has been developed. The estimations are based on aHidden Markov Model (HMM) using observations to determinethe driving style of surrounding vehicles. The model is trainedusing two different methods: Baum Welch training and Viterbitraining to improve the performance. Both training methods areevaluated by looking at time complexity and convergence. Themodel is implemented with and without training and tested fordifferent

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Book chapters on the topic "Baum Welch training"

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Rodríguez, Luis Javier, and Inés Torres. "Comparative Study of the Baum-Welch and Viterbi Training Algorithms Applied to Read and Spontaneous Speech Recognition." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_98.

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Conference papers on the topic "Baum Welch training"

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Huggins-Daines, David, and Alexander I. Rudnicky. "A constrained baum-welch algorithm for improved phoneme segmentation and efficient training." In Interspeech 2006. ISCA, 2006. http://dx.doi.org/10.21437/interspeech.2006-364.

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Kanevsky, Dimitri, Tara N. Sainath, Bhuvana Ramabhadran, and David Nahamoo. "Generalization of extended baum-welch parameter estimation for discriminative training and decoding." In Interspeech 2008. ISCA, 2008. http://dx.doi.org/10.21437/interspeech.2008-102.

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Hsiao, Roger, Yik-Cheung Tam, and Tanja Schultz. "Generalized Baum-Welch algorithm for discriminative training on large vocabulary continuous speech recognition system." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4960447.

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Cheshomi, Somayeh, Saeed Rahati-Q, and Mohammad-R. Akbarzadeh-T. "Hybrid of Chaos Optimization and Baum-Welch algorithms for HMM training in Continuous speech recognition." In 2010 International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2010. http://dx.doi.org/10.1109/icicip.2010.5565243.

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Zhang, Xueying, Yiping Wang, and Zhefeng Zhao. "A Hybrid Speech Recognition Training Method for HMM Based on Genetic Algorithm and Baum Welch Algorithm." In Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icicic.2007.33.

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