检索结果-南通市图书馆

hidden markov models with Factored Gaussian Mixtures Densities

在线全文

学校读者我要写书评

暂无评论

The Journal of China Universities of Posts and Telecommunications 2004年第3期11卷 74-78页

作者： LIHao-zheng LIUZhi-qiang ZHUXiang-hua School of Continuing Education BeijingUniversityofPostsandTelecommunicationsBeijing100876P.R.China SchoolofCreativeMediaCityUniversityofHongKong HongKongP.R.China

We present a factorial representation of Gaussian mixture models for observation densities in hidden markov models(HMMs), which uses the factorial learning in the HMM framework. We derive the reestimation formulas for estimating the factorized parameters by the Expectation Maximization (EM) algorithm. We conduct several experiments to compare the performance of this model structure with Factorial hidden markov models(FHMMs) and HMMs, some conclusions and promising empirical results are presented.

关键词： hidden markov models gaussian mixtures EM algorithm factorial learning

Recent Applications of hidden markov models in Computational Biology

在线全文

学校读者我要写书评

暂无评论

Genomics, Proteomics & Bioinformatics 2004年第2期2卷 84-96页

作者： KharHengChoo JooChuanTong： LouxinZhang DepartmentofBiochemistry NationalUniversityofSingapore10KentRidgeCrescentSingapore119260 DepartmentofMathematics NationalUniversityofSingapore2ScienceDrive2Singapore117543

This paper examines recent developments and applications of hidden markovmodels (HMMs) to various problems in computational biology, including multiple sequence alignment,homology detection, protein sequences classifi... 详细信息

关键词： hidden markov models sequence alignment homology detection proteinstructure prediction gene prediction

Tandem hidden markov models using deep belief networks for offline handwriting recognition

在线全文

学校读者我要写书评

暂无评论

Frontiers of Information Technology & Electronic Engineering 2017年第7期18卷 978-988页

作者： Partha Pratim ROY Guoqiang ZHONG Mohamed CHERIET Department of Computer Science & Engineering Indian Institute of Technology Roorkee Roorkee 24 766Z India Department of Computer Science and Technology Ocean University of China Qingdao 266100 China Synchromedia Laboratory Ecole de Technologie Supérieure Montreal H3C 1K3 Canada

Unconstrained offiine handwriting recognition is a challenging task in the areas of document analysis and pattern recognition. In recent years, to sufficiently exploit the supervisory information hidden in document images, much effort has been made to integrate multi-layer perceptrons （MLPs） in either a hybrid or a tandem fashion into hidden markov models （HMMs）. However, due to the weak learnability of MLPs, the learnt features are not necessarily optimal for subsequent recognition tasks. In this paper, we propose a deep architecture-based tandem approach for unconstrained offiine handwriting recognition. In the proposed model, deep belief networks arc adopted to learn the compact representations of sequential data, while HMMs are applied for （sub-）word recognition. We evaluate the proposed model on two publicly available datasets, i.e., RIMES and IFN/ENIT, which are based on Latin and Arabic languages respectively, and one dataset collected by ourselves called Devanagari （all Indian script）. Extensive experiments show the advantage of the proposed model, especially over the MLP-HMMs taudem approaches.

关键词： Handwriting recognition hidden markov models Deep learning Deep belief networks Tandemapproach

Fully Polarimetric Land Cover Classification Based on hidden markov models Trained with Multiple Observations

在线全文

学校读者我要写书评

暂无评论

Advances in Remote Sensing 2021年第3期10卷 102-114页

作者： Konstantinos Karachristos Georgia Koukiou Vassilis Anastassopoulos Electronics Laboratory (ELLAB) Physics Department University of Patras Patras Greece

A land cover classification procedure is presented utilizing the information content of fully polarimetric SAR images. The Cameron coherent target decomposition (CTD) is employed to characterize each pixel, using a set of canonical scattering mechanisms in order to describe the physical properties of the scatterer. The novelty of the proposed classification approach lies on the use of hidden markov models (HMM) to uniquely characterize each type of land cover. The motivation to this approach is the investigation of the alternation between scattering mechanisms from SAR pixel to pixel. Depending on the observations-scattering mechanisms and exploiting the transitions between the scattering mechanisms we decide upon the HMM-land cover type. The classification process is based on the likelihood of observation sequences been evaluated by each model. The performance of the classification approach is assessed my means of fully polarimetric SLC SAR data from the broader area of Vancouver, Canada and was found satisfactory, reaching a success from 87% to over 99%.

关键词： Fully Polarimetric SAR Coherent Decomposition Land Cover Classification hidden markov models Remote Sensing

A Hybrid Model of hidden markov models and a Selforganizing Neural Network Model in Speech Recognition

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A Hybrid Model of Hidden Markov Models and a Selforganizing ...

Differential colour-ring loss among Dalmatian Pelican(Pelecanus crispus) colonies and its consequence on survival estimates

2000年第16届世界计算机大会

作者： Li Jingjiao Sun Jie Li Yanquan Technology Center of Hisense Group School of Information Science and Engineering Northeastern University

＜正＞This paper proposed a hybrid model based on hidden markov models and a self-organizing neural network model,which can be used to speech *** algorithm of training and adjustment coefficient vectors is *** results... 详细信息

关键词： hidden markov models a self-organizing neural network model speech recognition

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Avian Research 2024年第1期15卷 66-71页

作者： Anastasios Bounas Giorgos Catsadorakis Dionyssia Hatzilacou Theodoros Naziridis Jocelyn Champagnon Alain J.Crivelli Department of Biological Applications and Technology University of IoanninaIoanninaGreece Society for the Protection of Prespa Agios GermanosGreece Natural Environment and Climate Change Agency 207 Mesogeion Ave.11525AthensGreece Natural Environment and Climate Change Agency Kerkini BranchKerkiniGR-62055Kato PoroiaGreece Tour du Valat Research Institute for Conservation of Mediterranean Wetlands13200ArlesFrance

The analysis of bird ringing data often comes with some potential sources of error and bias,as ring wear and/or loss could affect mark-recapture analyses and produce erroneous estimates of ***,ring wear and loss rates may differ between and within species based on the habitat they use or the species’ life-history traits and behaviour as well as the type of the *** this study we use resighting data from a long-term double marking experiment to directly estimate the rate of colour-ring loss among different Dalmatian Pelican colonies over time,evaluate any possible factors that could contribute to differential ring loss and assess how it may bias the results of mark-resighting *** on 14,849 resightings from 1275 individuals and using multi-state continuous-time hidden markov models(HMMs) we showed that probability of ring loss was markedly different among colonies,ranging from 0.10 to 0.42 within the first year of marking,whereas the cumulative probability of losing a ring after ten years ranged 0.64 to *** rates are among the highest estimated when compared to previous studies in *** approach assessing the intra-specific variance in ring loss provided several factors potentially involved,such as the use of glue and the fledgling age accuracy and we could further hypothesise the effect of environmental ***,our results showed that ring loss can be a significant challenge for the assessment of the species’ population dynamics using mark-recapture methods as survival was consistently underestimated when not accounting for ring loss and varied significantly among different colonies.

关键词： Bias Capture-mark-recapture Colour-ring Double-marking hidden markov models

A Study on the Impact of Voice-to-Text Technology on Academic Achievement of the Hearing-Impaired

在线全文

学校读者我要写书评

暂无评论

Journal of Contemporary Educational Research 2024年第8期8卷 276-282页

作者： Zhe Wang High School Affiliated to Renmin University of China Beijing 100000China

Hearing loss is a significant barrier to academic achievement,with hearing-impaired(HI)individuals often facing challenges in speech recognition,language development,and social ***-reading,a crucial skill for HI individuals,is essential for effective communication and ***,the COVID-19 pandemic has exacerbated the challenges faced by HI individuals,with the face masks hindering *** literature review explores the relationship between hearing loss and academic achievement,highlighting the importance of lip-reading and the potential of artificial intelligence(AI)techniques in mitigating these *** introduction of Voice-to-Text(VtT)technology,which provides real-time text captions,can significantly improve speech recognition and academic performance for HI *** models,such as hidden markov models and Transformer models,can enhance the accuracy and robustness of VtT technology in diverse educational ***,VtT technology can facilitate better teacher-student interactions,provide transcripts of lectures and classroom discussions,and bridge the gap in standardized testing performance between HI and hearing *** challenges and limitations exist,the successful implementation of VtT technology can promote inclusive education and enhance academic *** research directions include popularizing VtT technology,addressing technological barriers,and customizing VtT systems to cater to individual needs.

关键词： Lip-reading Hearing-impaired Voice-to-text Academic achievement hidden markov models Transformer models Inclusive education

Residual Chart with hidden markov Model to Monitoring the Auto-Correlated Processes

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Shanghai Jiaotong university(Science) 2018年第S1期23卷 103-108页

作者： LI Yaping HUANG Mengdie PAN Ershun College of Economics and Management Nanjing Forestry University Department of Industrial Engineering School of Mechanical Engineering Shanghai Jiao Tong University

Autocorrelations exist in real production extensively, and special statistical tools are needed for process monitoring. Residual charts based on autoregressive integrated moving average(ARIMA) models are typically used. However, ARIMA models need a quite amount of experience, which sometimes causes inconveniences in the implementation. With a good performance under less experience or even none, hidden markov models(HMMs)were proposed. Since ARIMA models have many different performances in positive and negative autocorrelations,it is interesting and essential to study how HMMs affect the performances of residual charts in opposite autocorrelations, which has not been studied yet. Therefore, we extend HMMs to negatively auto-correlated *** cross-validation method is used to select the relatively optimal state number. The experiment results show that HMMs are more stable than Auto-Regressive of order one(AR(1) models) in both cases of positive and negative autocorrelations. For detecting abnormalities, the performance of HMMs approach is much better than AR(1) models under positive autocorrelations while under negative autocorrelations both methods have similar performances.

关键词： auto-correlations hidden markov models statistical process control

Multi-Distributed Speech Emotion Recognition Based on Mel Frequency Cepstogram and Parameter Transfer

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2022年第1期31卷 155-167页

作者： LIN Long TAN Liang School of Computer Science Sichuan Normal University Institute of Computing Technology Chinese Academy of Sciences

Speech emotion recognition(SER) is the use of speech signals to estimate the state of emotion. At present, machine learning is one of the main research methods of SER, the test and training data S of traditional machine learning all have the same distribution and feature space, but the data of speech is accessed from different environments and devices, with different distribution characteristics in real life. Thus, the traditional machine learning method is applied to the poor performance of SER. This paper proposes a multi-distributed SER method based on Mel frequency cepstogram(MFCC) and parameter transfer. The method is based on single-layer long short-term memory(LSTM), pre-trained inceptionv3 network and multi-distribution corpus. The speech pre-processed MFCC is taken as the input of single-layer LSTM, and input to the pre-trained inception-v3 *** features are extracted through the pre-trained inception-v3 model. Then the features are sent to the newly defined the fully connected layer and classification layer,let the parameters of the fully connected layer be finetuned, finally get the classification result. The experiment proves that the method can effectively complete the classification of multi-distribution speech emotions and is more effective than the traditional machine learning framework of SER.

关键词： Speech recognition Emotion recognition Feature extraction Databases Training hidden markov models Speech enhancement