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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
31

Production Knowledge in the Recognition of Dysarthric Speech

Rudzicz, Frank 31 August 2011 (has links)
Millions of individuals have acquired or have been born with neuro-motor conditions that limit the control of their muscles, including those that manipulate the articulators of the vocal tract. These conditions, collectively called dysarthria, result in speech that is very difficult to understand, despite being generally syntactically and semantically correct. This difficulty is not limited to human listeners, but also adversely affects the performance of traditional automatic speech recognition (ASR) systems, which in some cases can be completely unusable by the affected individual. This dissertation describes research into improving ASR for speakers with dysarthria by means of incorporated knowledge of their speech production. The document first introduces theoretical aspects of dysarthria and of speech production and outlines related work in these combined areas within ASR. It then describes the acquisition and analysis of the TORGO database of dysarthric articulatory motion and demonstrates several consistent behaviours among speakers in this database, including predictable pronunciation errors, for example. Articulatory data are then used to train augmented ASR systems that model the statistical relationships between vocal tract configurations and their acoustic consequences. I show that dynamic Bayesian networks augmented with instantaneous theoretical or empirical articulatory variables outperform even discriminative alternatives. This leads to work that incorporates a more rigid theory of speech production, i.e., task-dynamics, that models the high-level and long-term aspects of speech production. For this task, I devised an algorithm for estimating articulatory positions given only acoustics that significantly outperforms the state-of-the-art. Finally, I present ongoing work into the transformation and re-synthesis of dysarthric speech in order to make it more intelligible to human listeners. This research represents definitive progress towards the accommodation of dysarthric speech within modern speech recognition systems. However, there is much more research that remains to be undertaken and I conclude with some thoughts as to which paths we might now take.
32

Facial Feature Point Detection

Chen, Fang 06 December 2011 (has links)
Facial feature point detection is a key issue in facial image processing. One main challenge of facial feature point detection is the variation of facial structures due to expressions. This thesis aims to explore more accurate and robust facial feature point detection algorithms, which can facilitate the research on facial image processing, in particular the facial expression analysis. This thesis introduces a facial feature point detection system, where the Multilinear Principal Component Analysis is applied to extract the highly descriptive features of facial feature points. In addition, to improve the accuracy and efficiency of the system, a skin color based face detection algorithm is studied. The experiment results have indicated that this system is effective in detecting 20 facial feature points in frontal faces with different expressions. This system has also achieved a higher accuracy during the comparison with the state-of-the-art, BoRMaN.
33

A High-performance Architecture for Training Viola-Jones Object Detectors

Lo, Charles 20 November 2012 (has links)
The object detection framework developed by Viola and Jones has become very popular due to its high quality and detection speed. However, the complexity of the computation required to train a detector makes it difficult to develop and test potential improvements to this algorithm or train detectors in the field. In this thesis, a configurable, high-performance FPGA architecture is presented to accelerate this training process. The architecture, structured as a systolic array of pipelined compute engines, is constructed to provide high throughput and make efficient use of the available external memory bandwidth. Extensions to the Viola-Jones detection framework are implemented to demonstrate the flexibility of the architecture. The design is implemented on a Xilinx ML605 development platform running at 200~MHz and obtains a 15-fold speed-up over a multi-threaded OpenCV implementation running on a high-end processor.
34

Astrometry.net: Automatic Recognition and Calibration of Astronomical Images

Lang, Dustin 03 March 2010 (has links)
We present Astrometry.net, a system for automatically recognizing and astrometrically calibrating astronomical images, using the information in the image pixels alone. The system is based on the geometric hashing approach in computer vision: We use the geometric relationships between low-level features (stars and galaxies), which are relatively indistinctive, to create geometric features that are distinctive enough that we can recognize images that cover less than one-millionth of the area of the sky. The geometric features are used to generate rapidly hypotheses about the location---the pointing, scale, and rotation---of an image on the sky. Each hypothesis is then evaluated in a Bayesian decision theory framework in order to ensure that most correct hypotheses are accepted while false hypotheses are almost never accepted. The feature-matching process is accelerated by using a new fast and space-efficient kd-tree implementation. The Astrometry.net system is available via a web interface, and the software is released under an open-source license. It is being used by hundreds of individual astronomers and several large-scale projects, so we have at least partially achieved our goal of helping ``to organize, annotate and make searchable all the world's astronomical information.''
35

Machine Learning Approaches to Biological Sequence and Phenotype Data Analysis

Min, Renqiang 17 February 2011 (has links)
To understand biology at a system level, I presented novel machine learning algorithms to reveal the underlying mechanisms of how genes and their products function in different biological levels in this thesis. Specifically, at sequence level, based on Kernel Support Vector Machines (SVMs), I proposed learned random-walk kernel and learned empirical-map kernel to identify protein remote homology solely based on sequence data, and I proposed a discriminative motif discovery algorithm to identify sequence motifs that characterize protein sequences' remote homology membership. The proposed approaches significantly outperform previous methods, especially on some challenging protein families. At expression and protein level, using hierarchical Bayesian graphical models, I developed the first high-throughput computational predictive model to filter sequence-based predictions of microRNA targets by incorporating the proteomic data of putative microRNA target genes, and I proposed another probabilistic model to explore the underlying mechanisms of microRNA regulation by combining the expression profile data of messenger RNAs and microRNAs. At cellular level, I further investigated how yeast genes manifest their functions in cell morphology by performing gene function prediction from the morphology data of yeast temperature-sensitive alleles. The developed prediction models enable biologists to choose some interesting yeast essential genes and study their predicted novel functions.
36

Kernel-based Copula Processes

Ng, Eddie Kai Ho 22 February 2011 (has links)
The field of time-series analysis has made important contributions to a wide spectrum of applications such as tide-level studies in hydrology, natural resource prospecting in geo-statistics, speech recognition, weather forecasting, financial trading, and economic forecasts and analysis. Nevertheless, the analysis of the non-Gaussian and non-stationary features of time-series remains challenging for the current state-of-art models. This thesis proposes an innovative framework that leverages the theory of copula, combined with a probabilistic framework from the machine learning community, to produce a versatile tool for multiple time-series analysis. I coined this new model Kernel-based Copula Processes (KCPs). Under the new proposed framework, various idiosyncracies can be modeled compactly via a kernel function for each individual time-series, and long-range dependency can be captured by a copula function. The copula function separates the marginal behavior and serial dependency structures, thus allowing them to be modeled separately and with much greater flexibility. Moreover, the codependent structure of a large number of time-series with potentially vastly different characteristics can be captured in a compact and elegant fashion through the notion of a binding copula. This feature allows a highly heterogeneous model to be built, breaking free from the homogeneous limitation of most conventional models. The KCPs have demonstrated superior predictive power when used to forecast a multitude of data sets from meteorological and financial areas. Finally, the versatility of the KCP model is exemplified when it was successfully applied to non-trivial classification problems unaltered.
37

Production Knowledge in the Recognition of Dysarthric Speech

Rudzicz, Frank 31 August 2011 (has links)
Millions of individuals have acquired or have been born with neuro-motor conditions that limit the control of their muscles, including those that manipulate the articulators of the vocal tract. These conditions, collectively called dysarthria, result in speech that is very difficult to understand, despite being generally syntactically and semantically correct. This difficulty is not limited to human listeners, but also adversely affects the performance of traditional automatic speech recognition (ASR) systems, which in some cases can be completely unusable by the affected individual. This dissertation describes research into improving ASR for speakers with dysarthria by means of incorporated knowledge of their speech production. The document first introduces theoretical aspects of dysarthria and of speech production and outlines related work in these combined areas within ASR. It then describes the acquisition and analysis of the TORGO database of dysarthric articulatory motion and demonstrates several consistent behaviours among speakers in this database, including predictable pronunciation errors, for example. Articulatory data are then used to train augmented ASR systems that model the statistical relationships between vocal tract configurations and their acoustic consequences. I show that dynamic Bayesian networks augmented with instantaneous theoretical or empirical articulatory variables outperform even discriminative alternatives. This leads to work that incorporates a more rigid theory of speech production, i.e., task-dynamics, that models the high-level and long-term aspects of speech production. For this task, I devised an algorithm for estimating articulatory positions given only acoustics that significantly outperforms the state-of-the-art. Finally, I present ongoing work into the transformation and re-synthesis of dysarthric speech in order to make it more intelligible to human listeners. This research represents definitive progress towards the accommodation of dysarthric speech within modern speech recognition systems. However, there is much more research that remains to be undertaken and I conclude with some thoughts as to which paths we might now take.
38

Facial Feature Point Detection

Chen, Fang 06 December 2011 (has links)
Facial feature point detection is a key issue in facial image processing. One main challenge of facial feature point detection is the variation of facial structures due to expressions. This thesis aims to explore more accurate and robust facial feature point detection algorithms, which can facilitate the research on facial image processing, in particular the facial expression analysis. This thesis introduces a facial feature point detection system, where the Multilinear Principal Component Analysis is applied to extract the highly descriptive features of facial feature points. In addition, to improve the accuracy and efficiency of the system, a skin color based face detection algorithm is studied. The experiment results have indicated that this system is effective in detecting 20 facial feature points in frontal faces with different expressions. This system has also achieved a higher accuracy during the comparison with the state-of-the-art, BoRMaN.
39

A High-performance Architecture for Training Viola-Jones Object Detectors

Lo, Charles 20 November 2012 (has links)
The object detection framework developed by Viola and Jones has become very popular due to its high quality and detection speed. However, the complexity of the computation required to train a detector makes it difficult to develop and test potential improvements to this algorithm or train detectors in the field. In this thesis, a configurable, high-performance FPGA architecture is presented to accelerate this training process. The architecture, structured as a systolic array of pipelined compute engines, is constructed to provide high throughput and make efficient use of the available external memory bandwidth. Extensions to the Viola-Jones detection framework are implemented to demonstrate the flexibility of the architecture. The design is implemented on a Xilinx ML605 development platform running at 200~MHz and obtains a 15-fold speed-up over a multi-threaded OpenCV implementation running on a high-end processor.
40

Knowledge Provenance: An Approach to Modeling and Maintaining The Evolution and Validity of Knowledge

Huang, Jingwei 28 July 2008 (has links)
The Web has become an open decentralized global information / knowledge repository, a platform for distributed computing and global electronic markets, where people are confronted with information of unknown sources, and need to interact with “strangers”. This makes trust and the validity of information in cyberspace arise as crucial issues. This thesis proposes knowledge provenance (KP) as a formal approach to determining the origin and validity of information / knowledge on the Web, by means of modeling and maintaining the information sources, information dependencies, and trust structures. We conceptualize and axiomatize KP ontology including static KP and dynamic KP. The proposed KP ontology, provides a formal representation of linking trust in information creators and belief in the information created; lays a foundation for further study of knowledge provenance; provides logical systems for provenance reasoning by machines. The web ontology of KP can be used to annotate web information; and KP reasoner can be used as a tool to trace the origin and to determine the validity of Web information. Since knowledge provenance is based on trust in information sources, this thesis also proposes a logical theory of trust in epistemic logic and situation calculus. In particular, we formally define the semantics of trust; from it, we identify two types of trust: trust in belief and trust in performance; reveal and prove that trust in belief is transitive; trust in performance is not, but by trust in belief, trust in performance can propagate in social networks; by using situation calculus in trust formalization, the context of trust is formally represented by reified fluents; we also propose a distributed logical model for trust reasoning using social networks, by which each agent’s private data about trust relationships can be protected. This study provides a formal theoretical analysis on the transitivity of trust, which supports trust propagation in social networks. This study of trust supports not only knowledge provenance but also the general trust modeling in cyberspace.

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