Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 68 for “"Hidden Markov Model: HMM)"”.
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A novel reduced-complexity approach to hidden Markov modeling of two-dimensional processes with application to face recognition.
The 2-D Hidden Markov Model (HMM) is an extension of the traditional 1-D HMM that has shown distinctive efficiency in modeling 1-D signals. Unlike 1-D HMMs, 2-D HMMs are known for their prohibitively high complexity. This encouraged many researchers to work on alternatives such as Pseudo 2-D HMM …
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Autoregressive hidden Markov models and the speech signal
This thesis introduces an autoregressive hidden Markov model (HMM) and demonstrates its application to the speech signal. This new variant of the HMM is built upon the mathematical structure of the HMM and linear prediction analysis of speech signals. By incorporating these two methods into one …
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Probabilistic insertion, deletion and substitution error correction using Markov inference in next generation sequencing reads
… insertion, deletion and substitution errors by modelling the sequencer output as emissions of an appropriately defined Hidden Markov Model (HMM). Reads are corrected to the corresponding maximum likelihood paths using an appropriately modified Viterbi algorithm. When compared with Karect and …
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Ground Target Tracking with Multi-Lane Constraint
… lane tracking is lane identification based ona Hidden Markov Model (HMM) framework. Two identifiers are developed according to different optimality goals of identification, i.e., the optimality for the whole lane sequence and the optimality of the current lane where the target is given the whole …
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Hidden Markov model based visual speech recognition
… processing are presented. Classifiers based on Hidden Markov Model (HMM) are first explored for modeling and identifying the basic visual speech elements. Considering that the temporal features of visual speech elements may be confusable and sensitive to their contexts, three novel training …
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Multi-tape finite-state transducer for asynchronous multi-stream pattern recognition with application to speech
… we have focused on improving the acoustic modeling of speech recognition systems to increase the overall recognition performance. We formulate a novel multi-stream speech recognition framework using multi-tape finite-state transducers (FSTs). The multi-dimensional input labels of the …
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Factor graphs and MCMC approaches to iterative equalization of nonlinear dispersive channels
… problem into forward-backward algorithm on hidden Markov model (HMM). The equalizer is implemented via the sum-product algorithm on the factor graph representation of the channel and receiver blocks. The second equalization strategy is based on Markov chain Monte Carlo (MCMC) methods. We …
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Parameter estimation in HMMs with guaranteed convergence
… for parameter estimation in statistical models with latent variables, where explicit computation of the maximum likelihood estimate (MLE) is infeasible. Although widely used in practice, the theoretical guarantees associated with EM are quite weak. We study the setting of a hidden Markov …
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Generative Models for Retrieval of Video, Audio and Text Data
… basic idea of our approach is to first train a hidden Markov model (HMM) using the given example, it is called the theme HMM. The total audio data available is used to train a background HMM. We combine these individual HMMs to form a synthesized ""background-theme-background"" HMM. This …
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Temporal registration for MRI time series
… and deformations in MRI time series. We make a Markov assumption on the nature of deformations to take advantage of the temporal smoothness in the image data. Forward message passing in the corresponding hidden Markov model (HMM) yields an estimation algorithm that only has to account for …
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Generating realistic, animated human gestures in order to model, analyse and recognize Irish Sign Language
… system and has been implemented as a Hidden Markov Model (HMM).
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Multimodal Fusion With Applications to Audio -Visual Speech Recognition
… for both applications. For audio-visual speech modeling, we propose a novel sensory fusion method based on the coupled hidden Markov models (CHMMs). The CHMM framework allows the fusion of two temporally coupled information sources to take place as an integral part of the statistical modeling …
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Surface electromyography based speech recognition system and development toolkit
… Classification was performed using a hidden Markov model (HMM). The system implemented was able to achieve an accuracy rate of 74.24% with E4-NC and 61.25% with E4-C. These results are comparable to previously reported results for offline, single session, isolated word recognition. …
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Automatic Phoneme Recognition with Segmental Hidden Markov Models
… description of the integrated elements. The Hidden Markov Model (HMM) based phoneme models are trained using the Baum-Welch re-estimation procedure. Recognition and segmentation of the phonemes in the continuous speech is performed by a Segmental Viterbi Search on a Segmental Ergodic HMM for …
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On improving the forecast accuracy of the hidden Markov model
The forecast accuracy of a hidden Markov model (HMM) may be low due first, to the measure of forecast accuracy being ignored in the parameterestimation method and, second, to overfitting caused by the large number of parameters that must be estimated. A general approach to forecasting is described …
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Android Application Install-time Permission Validation and Run-time Malicious Pattern Detection
… malicious patterns in runtime, we present a Hidden Markov Model (HMM) method to analyze the activity usage by tracking Intent log information. After applying our technique to nearly 1,700 popular third-party Android applications and malware, we report that a major portion of the category …
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Application of Kalman Filtering to Real-time Flight Regime Recognition Algorithms in a Helicopter Health and Usage Monitoring System
… developed FRR algorithms successfully applied Hidden Markov Models, which are similar to Kalman filters. The selected regime set for this study derives from a study performed by Bell Helicopter Textron, Inc. The selected parameter set for this study is modified from the Schweizer 300 Flight …
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Application of Hidden Markov Model in Finite Mixture Modeling of High-Dimensional Data
Finite mixture models (FMMs) are widely used in practice and are famous for modeling heterogeneous data in a convenient and effective way. Owing to their flexibility, finitemixtures have since been applied to a wide range of problems in diverse fields, includingimage analysis, medicine, …
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Card counting meets hidden Markov models
The Hidden Markov Model (HMM) is a stochastic process that involves an unobservable Markov Chain and an observable output at each state in the chain. Hidden Markov Models are described by three parameters: A, B, and \uf070. A is a matrix that holds the transition probabilities for the unobservable …
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Applications of broad class knowledge for noise robust speech recognition
… signal. Given an initial set of broad class models and input speech data, we explore a gradient steepness metric using the Extended Baum-Welch (EBW) transformations to explain how much these initial model must be adapted to fit the target data. We incorporate this gradient metric into a …
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