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检索条件"主题词=Baum-welch algorithm"

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Automatic Classification of Disordered Voices with Hidden Markov Models

Automatic Classification of Disordered Voices with Hidden Ma...

引用

International Conference on Signal, Image, Vision and their Applications (SIVA)

作者： Benhammoud, Redouane Kacha, Abdellah Univ Mohamed Seddik Ben Yahia Lab Phys Reyonnement & Applicat Jijel Algeria

ISBN: (纸本)9781538671207

The auditory-based method is commonly used in the assessment of voice disorders. This method is subjective in the sense that the evaluation result depends on the listener and a great deal of expertise is required to obtain reproducible evaluations. Automatic assessment of disordered voices provides a support for clinicians to establish a better assessment of the disorder. The main concern of this article is the clinical evaluation of disordered voices by using an automatic classification method based on Hidden Markov Models (HMMs). The HMM classification technique is based on a learning theory that uses baum-welch algorithm, a special case of the EM algorithm. The descriptors that are used as inputs to the HMM classifier are the cepstral MFCC coefficients. The performances of the classification method that is supervised, in terms of classification accuracy are investigated using a database consisting of a concatenation of two Dutch-speaking sentences produced by 28 normophonic and 223 dysphonic speakers.

关键词： disordered voices classification Hidden Markov Models baum-welch algorithm Viterbi algorithm mel frequency cepstral coefficients

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A Prognostic Method for Radar Transmitter Based on DHMM 8

A Prognostic Method for Radar Transmitter Based on DHMM

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2017 Prognostics and System Health Management Conference (PHM-Harbin)

作者： Yu, Pengfei Zhou, Zhenwei Cheng, Liye Lu, Yudong Minist Ind & Informat Technol Elect Res Inst 5 Sci & Technol Reliabil Phys & Applicat Elect Comp Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538603703

For the randomness and uncertainty of fault for Radar transmitter, a prognostic method based on discrete Hidden Markov Model (DHMM) is proposed. In the paper, three monitoring parameters of transmitter are collected and a discrete Hidden Markov Model is established. In order to have a fast convergence, The baum-welch (B-W) algorithm is used for training of DHMM. Finally, the state probability transition curve and reliability transition curve of the transmitter is acquired, therefore health assessment is achieved. The experimental results show that proposed method is a practical method with accuracy and stability.

关键词： transmitter discrete Hidden Markov Model baum-welch algorithm health assessment

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Dynamic and Interactive Gesture Recognition algorithm Based on Kinect 29

Dynamic and Interactive Gesture Recognition Algorithm Based ...

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第29届中国控制与决策会议

作者： Guangjun Dai Lei Yu Jun Huang College of Mechanical and Electrical Engineering Soochow University

ISBN: (纸本)9781509046584

With the constant development of smart devices, Gesture recognition is applied on more and more fields. Gesture recognition devices currently on the market are inconvenient and expensive. A gesture recognition method based on Kinect is proposed in this paper. The camera of Kinect is used to get gesture images and then a hidden Markov model is established to recognize dynamic gesture so that the operation is simple. The assessment, decoding algorithms and training issues of hidden Markov model in gesture recognition is focused in this paper. Viterbi algorithm and baum-welch algorithm are used to solve the problems above. After simulation experiments, it is discovered that the gesture recognition algorithm based on Kinect proposed in this paper has a high acc uracy rate for a variety of gestures, simultaneously the naturalness of human-computer interaction has been greatly improved.

关键词： Gesture recognition Kinect Hidden Markov models Viterbi algorithm baum-welch algorithm

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Large Vocabulary Speech Recognition: Speaker Dependent and Speaker Independent 1

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2nd International Conference on Information Systems Design and Intelligent Applications (INDIA)

作者： Hemakumar, G. Punitha, P. Govt Coll Women Dept Comp Sci Mandya India PESIT Dept MCA Bangalore Karnataka India

ISBN: (数字)9788132222507

ISBN: (纸本)9788132222507;9788132222491

This paper addresses the problem of large vocabulary isolated word and continuous Kannada speech recognition using the syllables and combination of Hidden Markov Model (HMM) and Normal fit method. The models designed for speaker dependent and speaker independent mode of working. This experiment has covered 6 million words among the 10 million words from Hampi text corpus. Here 3-state baum-welch algorithm is used for training. For the 2 successor outputted lambda(A, B, pi) is combined and passed into normal fit, the outputted normal fit parameter is labeled has syllable or sub-word. In terms of memory requirement and recognition rate the proposed model is compared with Gaussian Mixture Model and HMM (3-state baum-welch algorithm). This paper clearly shows that combination of HMM and normal fit technique will reduce the memory size while building and storing the speech models and works with excellent recognition rate. The average WRR is 91.22 % and average WER is 8.78 %. All computations are done using mat lab.

关键词： Speaker independent Speaker dependent Normal fit baum-welch algorithm

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Re-estimation of Hidden Markov Model parameters of Phasor Measurement Unit

Re-estimation of Hidden Markov Model parameters of Phasor Me...

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IEEE Power, Communication and Information Technology Conference (PCITC)

作者： Murthy, Ch Roy, D. S. Mohanta, D. K. Natl Inst Sci & Technol Dept EEE Berhampur Orissa India Natl Inst Sci & Technol Dept CSE Berhampur Orissa India MVGR Coll Engn Dept EEE Vizianagaram 535005 India

ISBN: (纸本)9781479974559

In recent times, the power system has expanded in an unprecedented way owing to technological innovations and geographical dimensions. For ensuring proper operation, monitoring and control, a system called Wide Area Measurement System (WAMS) has evolved. Phasor Measurement Unit (PMU) is one of the vital components of WAMS. Any failure in PMU has catastrophic consequences on WAMS. Thus reliability analysis of PMU is of utmost importance. Hidden Markov model (HMM) is one of the important techniques for evaluating reliability. However, while evaluating the reliability of PMU using HMM, the HMM parameters such as state transition probability matrix (A), observation probability matrix (B) and initial state probability (pi) are either assumed or have been derived from related research papers. In this paper, a baum-welch algorithm has been used to re-estimate these HMM parameters of the seven modules of PMU to best fit the observations.

关键词： hidden Markov model phasor measurement unit reliability baum-welch algorithm

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Real-time daily activity classification with wireless sensor networks using hidden Markov model

Real-time daily activity classification with wireless sensor...

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29th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society

作者： He, Jin Li, Huaming Tan, Jindong Michigan Technol Univ Dept Elect & Comp Engn Houghton MI 49931 USA

ISBN: (纸本)9781424407873

This paper presents a Hidden Markov Model (HMM) approach for real-time activity classification using signals from wearable wireless sensor networks. A wearable wireless sensor network can be used to continuously monitor the daffy activities of a subject in real time. However, the wireless sensor nodes are constrained by limited battery and computing resources. The proposed HMM framework has been applied to find the most probable activity states series with low data transmission rate, which makes it highly suitable for daily activity classification applications. The performance was evaluated using a small sensor network consisting of three accelerometers. The activity detection rate is 95.82%, using a test set of 5 subjects with 11 activity series.

关键词： activity classification hidden Markov models viterbi algorithm baum-welch algorithm accelerometer

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Training Second-Order Hidden Markov Models with Multiple Observation Sequences

Training Second-Order Hidden Markov Models with Multiple Obs...

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International Forum on Computer Science-Technology and Applications

作者： Du Shiping Chen Tao Zeng Xianyin Wang Jian Wei Yuming Sichuan Agr Univ Coll Biol & Sci Yaan 625014 Sichuan Peoples R China Sichuan Agr Univ Triticeae Res Inst Yaan 625014 Sichuan Peoples R China

ISBN: (纸本)9780769539300

Second-order hidden Markov models (HMM2) have been widely used in pattern recognition, especially in speech recognition. Their main advantages are their capabilities to model noisy temporal signals of variable length. In this article, we introduce a new HMM2 with multiple observable sequences, assuming that all the observable sequences are statistically correlated. In this treatment, the multiple observation probability is expressed as a combination of individual observation probabilities without losing generality. This combinatorial method gives one more freedom in making different dependence-independence assumptions. By generalizing baum's auxiliary function into this framework and building up an associated objective function using Lagrange multiplier method, several new formulae solving model training problem are theoretically derived, we show that the model training equations can be easily derived with an independence assumption.

关键词： Second-order hidden Markov models forward-backward procedure baum-welch algorithm multiple observable sequences

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Towards Efficient and Privacy-Preserving Federated Learning for HMM Training

Towards Efficient and Privacy-Preserving Federated Learning ...

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IEEE Conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Zheng, Yandong Zhu, Hui Lu, Rongxing Zhang, Songnian Guan, Yunguo Wang, Fengwei Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Univ New Brunswick Fac Comp Sci Fredericton NB E3B 5A3 Canada

ISBN: (纸本)9798350310900

The hidden Markov model (HMM) has played a pivotal role in various IoT applications due to its ability to model time-varying sequences. Since the datasets usually live in isolated islands and their privacy naturally demands to be seriously considered, the HMM should be trained in a privacy-preserving manner. A typical HMM training framework is federated learning, in which a federated server and many data owners collaboratively train an HMM without revealing data owners' data to the federated server and the trained model to data owners. Since existing HMM training schemes are computationally intensive, we propose an efficient and privacy-preserving federated learning scheme for HMM training to address the efficiency issue in this paper. First, we transform all HMM training computations into matrices- and vectors-based computations over real domains. Then, we introduce our federated HMM training scheme by applying matrix encryption to protect the HMM training privacy. After that, we show that our scheme is privacy-preserving through a rigorous analysis on the security of our scheme. We illustrate that our scheme is efficient through extensive experimental evaluation on the performance of our scheme.

关键词： Federated learning Hidden Markov model baum-welch algorithm Matrix encryption

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Stochastic Interpretation of Universal Portfolio and Generalized Target Classes

Stochastic Interpretation of Universal Portfolio and General...

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IEEE International Symposium on Information Theory (ISIT)

作者： Tsurusaki, Mariko Takeuchi, Jun'ichi Kyushu Univ Fac Informat Sci & Elect Engn Nishi Ku Fukuoka 8190395 Japan

ISBN: (纸本)9781457705953

We provide a new look at Cover's universal portfolio, where we define probability density functions (p.d.f.) representing wealth functions of portfolios. In this view, log wealth ratio of a portfolio sequence is equal to coding regret of its p.d.f. for the target class which consists of the p.d.f. representing constantly rebalanced portfolios (CRP). It is revealed that the p.d.f. of a CRP is a hidden Markov model (HMM) with the restriction that the latent variable's distribution is Bernoulli. Further we consider the portfolio with the generalized target class defined by extending the latent variable's distribution to a parametric model of stochastic processes. Then, we discuss the minimax log wealth ratio of the class analyzing Fisher information of the p.d.f. for portfolios, which is strictly smaller than that of the latent variable's model. Finally we propose a portfolio strategy using the Jeffreys prior of the class of p.d.f. and an efficient method to calculate causal portfolios using the baum-welch algorithm.

关键词： baum-welch algorithm Bernoulli distribution CRP Data compression Data models Encoding Fisher information Hidden Markov models Markov processes Portfolios coding constantly rebalanced portfolios encoding generalized target class hidden Markov model hidden Markov models latent variables distribution log wealth ratio parametric model probability density functions statistical distributions stochastic interpretation universal portfolio

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Application of the Hidden Markov Model for Innovative Projects "Viability" Analysis 19

Application of the Hidden Markov Model for Innovative Projec...

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19th IEEE International Conference on Soft Computing and Measurements (SCM)

作者： Rashidov, Aleksandr R. Shmidt, Igor A. Perm Natl Res Polytech Univ MMA Dept Perm Russia

ISBN: (纸本)9781467389198

This article deals with determining of innovative projects "viability". "Viability" is the probability of innovative project being implemented. Hidden Markov Models are used for evaluation of this factor. The problem of determining parameters of model, which produce given data sequence with the highest probability, are solving in this research. The baum-welch algorithm which is one implementation of Expectation-Maximization algorithm is used at this research for calculating model parameters. At the end part of the article mathematical basics for practical implementation are given (in particular mathematical description of the algorithm and implementation methods for Markov models).

关键词： Innovative project Markov process Hidden Markov model baum-welch algorithm project "viability"

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