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Optimization of HMM by the tabu search algorithm

JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2004年第5期20卷 949-957页

作者： Chen, TY Mei, XD Pan, JS Sun, SH Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 807 Taiwan Harbin Inst Technol Dept Automat Test Measurement & Control Harbin 150001 Peoples R China

In this paper, a simple version of the tabu search algorithm is employed to train a Hidden Markov Model (HMM) to search out the optimal parameter structure of HMM for automatic speech recognition. The proposed TS-HMM training provides a mechanism that allows the search process to escape from a local optimum and obtain a near global optimum. Experimental results show that the TS-HMM training has a higher probability of finding the optimal model parameters than traditional algorithms do.

关键词： tabu search hidden Markov model forward-backward algorithm speech recognition global optimum

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Continuous Dynamics Related to Monotone Inclusions and Non-Smooth Optimization Problems

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SET-VALUED AND VARIATIONAL ANALYSIS 2020年第4期28卷 611-642页

作者： Csetnek, Erno Robert Univ Vienna Fac Math Oskar Morgenstern Pl 1 A-1090 Vienna Austria

The aim of this survey is to present the main important techniques and tools from variational analysis used for first and second order dynamical systems of implicit type for solving monotone inclusions and non-smooth optimization problems. The differential equations are expressed by means of the resolvent (in case of a maximally monotone set valued operator) or the proximal operator for non-smooth functions. The asymptotic analysis of the trajectories generated relies on Lyapunov theory, where the appropriate energy functional plays a decisive role. While the most part of the paper is related to monotone inclusions and convex optimization problems in the variational case, we present also results for dynamical systems for solving non-convex optimization problems, where the Kurdyka-Lojasiewicz property is used.

关键词： Dynamical systems Lyapunov analysis Krasnosel'skii-Mann algorithm Monotone inclusions Resolvent Proximal operator forward-backward algorithm Non-smooth optimization problem Kurdyka-Lojasiewicz property

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Finite mixture of hidden Markov models for tensor-variate time series data

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ADVANCES IN DATA ANALYSIS AND CLASSIFICATION 2024年第3期18卷 545-562页

作者： Asilkalkan, Abdullah Zhu, Xuwen Sarkar, Shuchismita Univ Alabama Dept Informat Syst Stat & Management Sci Tuscaloosa AL 35487 USA Bowling Green State Univ Dept Appl Stat & Operat Res Bowling Green OH 43403 USA

The need to model data with higher dimensions, such as a tensor-variate framework where each observation is considered a three-dimensional object, increases due to rapid improvements in computational power and data storage capabilities. In this study, a finite mixture of hidden Markov model for tensor-variate time series data is developed. Simulation studies demonstrate high classification accuracy for both cluster and regime IDs. To further validate the usefulness of the proposed model, it is applied to real-life data with promising results.

关键词： Finite mixture model Hidden Markov model forward-backward algorithm Tensor-variate time series

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Efficient MMSE-Based channel error mitigation techniques.: Application to distributed speech recognition over wireless channels

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2005年第1期4卷 14-19页

作者： Peinado, AM Sánchez, V Pérez-Córdoba, JL Rubio, AJ Univ Granada Fac Ciencias Dept Teorfia Senal Telemat & Commun E-18071 Granada Spain

This work addresses the mitigation of channel errors by means of efficient minimum mean-square-error (MMSE) estimation. Although powerful model-based implementations have been recently proposed, the computational burden involved can make them impractical. We propose two new approaches that maintain a good level of performance with a low computational complexity. These approaches keep the simple structure and complexity of a raw MMSE estimation, although they enhance it with additional source a priori knowledge. The proposed techniques are built on a distributed speech recognition system. Different degrees of tradeoff between recognition performance and computational complexity are obtained.

关键词： channel error mitigation distributed speech recognition forward-backward algorithm hidden Markov models minimum mean-square-error (MMSE) estimation wireless channels

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Adaptive Marker Coding for Insertion/Deletion/Substitution Error Correction

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2014年第2期E97A卷 642-651页

作者： Inoue, Masato Kaneko, Haruhiko Tokyo Inst Technol Grad Sch Informat Sci & Engn Tokyo 1528552 Japan

This paper proposes an adaptive marker coding (AMC) for correction of insertion/deletion/substitution errors. Unlike the conventional marker codings which select marker-bit values deterministically, the AMC adaptively reverses the first and last bits of each marker as well as bits surrounding the marker. Decoding is based on a forward-backward algorithm which takes into account the dependency of bit-values around the marker. Evaluation shows that, for a channel with insertion/deletion error probability 1.8 x 10(-2), the decoded BER of existing marker coding of rate 9/16 is 4.25 x 10(-3), while that of the proposed coding with the same code rate is 1.73 x 10(-3).

关键词： insertion/deletion error marker code LDPC code forward-backward algorithm

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Virtual Full-Duplex Distributed Spatial Modulation: A New Protocol for Cooperative Diversity

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IEEE ACCESS 2019年 7卷 87634-87646页

作者： Shehni, Amir Flanagan, Mark F. Univ Coll Dublin Sch Elect & Elect Engn Dublin D04 V1W8 Ireland

Spatial modulation, a multi-antenna technology which uses the antenna index as an additional means of conveying information, is an emerging technology for modem wireless communications. In this paper, a new distributed version of spatial modulation is proposed which achieves virtual full-duplex communication (VFD-DSM) in order to increase system throughput. This throughput improvement is achieved by allowing the source to transmit new data while the relays implicitly forward the source's data in every time slot (via the index of the active relay) while explicitly transmitting their own data (via a conventional modulation technique). Motivated by the achievable throughput improvement with VFD-DSM, two maximum a posteriori (MAP) detection methods are proposed for implementation at the destination node: the first, called local MAP, is based on processing the signals received over two or three consecutive time slots, while the second, called global MAP, is based on symbol-error-rate optimal detection over an entire frame of data. For each MAP detection method, an error-aware version of the detector is also proposed which takes into account the demodulation error rate at the relays;this can achieve an extra BER advantage at the cost of additional complexity and an increased channel state information (CSI) requirement. Simulation results demonstrate that the proposed VFD-DSM protocol can provide an improved BER compared to the baseline protocol of successive relaying, while also providing a significant increase in the overall throughput since the relays can forward the source symbols while simultaneously transmitting their own data. The proposed VFD-DSM detectors are shown to provide a range of design choices offering different tradeoffs between BER performance and computational complexity. Finally, the impact of the data frame length in VFD-DSM on the error rate performance and system throughput is investigated, and it is shown how to choose the optimal frame length for

关键词： Spatial modulation full-duplex communication relay networks forward-backward algorithm

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Decoding on Adaptively Pruned Trellis for Correcting Synchronization Errors

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China Communications 2017年第7期14卷 163-171页

作者： Yuan Liu Weigang Chen School of Microelectronics Tianjin Univeristy

forward-backward algorithm, used by watermark decoder for correcting non-binary synchronization errors, requires to traverse a very large scale trellis in order to achieve the proper posterior probability, leading to high computational complexity. In order to reduce the number of the states involved in the computation, an adaptive pruning method for the trellis is proposed. In this scheme, we prune the states which have the low forward-backward quantities below a carefully-chosen threshold. Thus, a wandering trellis with much less states is achieved, which contains most of the states with quite high probability. Simulation results reveal that, with the proper scaling factor, significant complexity reduction in the forward-backward algorithm is achieved at the expense of slight performance degradation.

关键词： forward-backward algorithm non-binary synchronization errors adaptive pruning method complexity reduction

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HMM-based channel error mitigation and its application to distributed speech recognition

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SPEECH COMMUNICATION 2003年第4期41卷 549-561页

作者： Peinado, AM Sánchez, V Pérez-Córdoba, JL de la Torre, A Univ Granada Fac Ciencias Dept Elect & Tecnol Comp Granada 18071 Spain

The emergence of distributed speech recognition has generated the need to mitigate the degradations that the transmission channel introduces in the speech features used for recognition. This work proposes a hidden Markov model (HMM) framework from which different mitigation techniques oriented to wireless channels can be derived. First, we study the performance of two techniques based on the use of a minimum mean square error (MMSE) estimation, a raw MMSE and a forward MMSE estimation, over additive white Gaussian noise (AWGN) channels. These techniques are also adapted to bursty channels. Then, we propose two new mitigation methods specially suitable for bursty channels. The first one is based on a forward-backward MMSE estimation and the second one on the well-known Viterbi algorithm. Different experiments are carried out, dealing with several issues such as the application of hard decisions on the received bits or the influence of the estimated channel SNR. The experimental results show that the HMM-based techniques can effectively mitigate channel errors, even in very poor channel conditions. (C) 2003 Elsevier B.V. All rights reserved.

关键词： channel error mitigation distributed speech recognition hidden Markov models wireless communications forward-backward algorithm Viterbi algorithm

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Approximate posterior distributions for convolutional two-level hidden Markov models

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2013年第1期58卷 187-200页

作者： Rimstad, Kjartan Omre, Henning Norwegian Univ Sci & Technol Dept Math Sci N-7034 Trondheim Norway

A convolutional two-level hidden Markov model is defined and evaluated. The bottom level contains an unobserved categorical Markov chain, and given the variables in this level the middle level contains unobserved conditionally independent Gaussian variables. The top level contains observable variables that are a convolution of the variables in the middle level plus additive Gaussian errors. The objective is to assess the categorical variables in the bottom level given the convolved observations in the top level. The inversion is cast in a Bayesian setting with a Markov chain prior model and convolved Gaussian likelihood model. The associated posterior model cannot be assessed since the normalizing constant is too computer demanding to calculate for realistic problems. Three approximate posterior models based on approximations of the likelihood model on generalized factorial form are defined. These approximations can be exactly assessed by the forward-backward algorithm. Both a synthetic case and a real seismic inversion case are used in an empirical evaluation. It is concluded that reliable and computationally efficient approximate posterior models for convolutional two-level hidden Markov models can be defined. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Approximation Convolution Hidden Markov model forward-backward algorithm Seismic inversion

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High-order extensions of the double chain Markov model

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STOCHASTIC MODELS 2002年第2期18卷 193-227页

作者： Berchtold, A Univ Lausanne Inst Appl Math SSP BFSH2 CH-1015 Lausanne Switzerland Univ Washington Seattle WA 98195 USA

The Double Chain Markov Model is a fully Markovian model for the representation of time-series in random environments. In this article, we show that it can handle transitions of high-order between both a set of observations and a set of hidden states. In order to reduce the number of parameters, each transition matrix can be replaced by a Mixture Transition Distribution model. We provide a complete derivation of the algorithms needed to compute the model. Three applications, the analysis of a sequence of DNA, the song of the wood pewee, and the behavior of young monkeys show that this model is of great interest for the representation of data that can be decomposed into a finite set of patterns.

关键词： double chain Markov model (DCMM) high-order transitions mixture transition distribution model (MTD) forward-backward algorithm Baum-Welch algorithm viterbi algorithm DNA pewee behavior

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