Thesis (M. S.)--Music, Georgia Institute of Technology, 2009. / Committee Chair: Chordia, Parag; Committee Member: Freeman, Jason; Committee Member: Weinberg, Gil.

Zugl.: Münster, Univ., Diss., 2007.

Thesis (Ph. D.) University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 2, 2007) Includes bibliographical references.

Universiẗat, Diss., 2004--Kiel.

University, Diss., 2005--Bielefeld.

Philosophiae Doctor - PhD / In this thesis, we present a novel hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov Models (HMMs). Though sequence recognition problems could be potentially modelled through well trained HMMs, they could not provide a reasonable solution to the complicated recognition problems. In contrast, the ability of RNNs to recognize the complex sequence recognition problems is known to be exceptionally good. It should be noted that in the past, methods for applying HMMs into RNNs have been developed by other researchers. However, to the best of our knowledge, no algorithm for processing HMMs through learning has been given. Taking advantage of the structural similarities of the architectural dynamics of the RNNs and HMMs, in this work we analyze the combination of these two systems into the hybrid architecture. To this end, the main objective of this study is to improve the sequence recognition/classi_cation performance by applying a hybrid neural/symbolic approach. In particular, trained HMMs are used as the initial symbolic domain theory and directly encoded into appropriate RNN architecture, meaning that the prior knowledge is processed through the training of RNNs. Proposed algorithm is then implemented on sample test beds and other real time biological applications.

The collection of large speech databases is not a trivial task (if done properly). It is not always possible to collect, segment and annotate large databases for every task or language. It is also often the case that there are imbalances in the databases, as a result of little data being available for a specific subset of individuals. An example of one such imbalance is the fact that there are often more male speakers than female speakers (or vice-versa). If there are, for example, far fewer female speakers than male speakers, then the recognizers will tend to work poorly for female speakers (as compared to performance for male speakers). This thesis focuses on using Bayesian and discriminative training algorithms to improve continuous speech recognition systems in scenarios where there is a limited amount of training data available. The research reported in this thesis can be divided into three categories: • Overspecialization is characterized by good recognition performance for the data used during training, but poor recognition performance for independent testing data. This is a problem when too little data is available for training purposes. Methods of reducing overspecialization in the minimum classification error algo¬rithm are therefore investigated. • Development of new Bayesian and discriminative adaptation/training techniques that can be used in situations where there is a small amount of data available. One example here is the situation where an imbalance in terms of numbers of male and female speakers exists and these techniques can be used to improve recognition performance for female speakers, while not decreasing recognition performance for the male speakers. • Bayesian learning, where Bayesian training is used to improve recognition perfor¬mance in situations where one can only use the limited training data available. These methods are extremely computationally expensive, but are justified by the improved recognition rates for certain tasks. This is, to the author's knowledge, the first time that Bayesian learning using Markov chain Monte Carlo methods have been used in hidden Markov model speech recognition. The algorithms proposed and reviewed are tested using three different datasets (TIMIT, TIDIGITS and SUNSpeech), with the tasks being connected digit recognition and con¬tinuous speech recognition. Results indicate that the proposed algorithms improve recognition performance significantly for situations where little training data is avail¬able. / Thesis (PhD (Electronic Engineering))--University of Pretoria, 2006. / Electrical, Electronic and Computer Engineering / unrestricted

Most of the chronic diseases have a well-known natural staging system through which the disease progression is interpreted. It is well established that the transition rates from one stage of disease to other stage can be modeled by multi state Markov models. But, it is also well known that the screening systems used to diagnose disease states may subject to error some times. In this study, a simulation study is conducted to illustrate the importance of addressing for misclassification in multi-state Markov models by evaluating and comparing the estimates for the disease progression Markov model with misclassification opposed to disease progression Markov model. Results of simulation study support that models not accounting for possible misclassification leads to bias. In order to illustrate method of accounting for misclassification is illustrated using dementia data which was staged as no cognitive impairment, mild cognitive impairment and dementia and diagnosis of dementia stage is prone to error sometimes. Subjects entered the study irrespective of their state of disease and were followed for one year and their disease state at follow up visit was recorded. This data is used to illustrate that application of multi state Markov model which is an example of Hidden Markov model in accounting for misclassification which is based on an assumption that the observed (misclassified) states conditionally depend on the underlying true disease states which follow the Markov process. The misclassification probabilities for all the allowed disease transitions were also estimated. The impact of misclassification on the effect of covariates is estimated by comparing the hazard ratios estimated by fitting data with progression multi state model and by fitting data with multi state model with misclassification which revealed that if misclassification has not been addressed the results are biased. Results suggest that the gene apoe ε4 is significantly associated with disease progression from mild cognitive impairment to dementia but, this effect was masked when general multi state Markov model was used. While there is no significant relation is found for other transitions.

No description available.

In this thesis the Hidden Markov Model (HMM) is used in the process of map matching to investigate the accuracy for road segment map matching. A few HMM algorithms using a Global Navigation Satellite System (GNSS) receiver, odometer and gyroscope sensors are presented. The HMM algorithms are evaluated on four accuracy metrics. Two of these metrics have been seen in previous literature and captures road map match accuracy. The other have not been seen before and captures road segment accuracy. In the evaluation process a dataset is created by simulation to achieve positional ground truth for each sensor measurement. The accuracy distribution for different parts of the map matched trajectory is also evaluated. The result shows that HMM algorithms presented in previous literature, falls short to capture the accuracy for road segment map matching. The results further shows that by using less noisy sensors, as odometer and gyroscope, the accuracy for road segment map matching can be increased.