An exploratory study of foreign accent and phonological awareness in Korean learners of English

Communication in a second or multiple languages has become essential in the globalized world. However, acquiring a second language (L2) after a critical period is universally acknowledged to be challenging (Lenneberg, 1967). Late learners hardly reach a nativelike level in L2, particularly in its pronunciation, and their incomplete phonological acquisition is manifested by a foreign accent—a common and persistent feature of otherwise fluent L2 speech. Although foreign-accented speech is widespread, it has been a target of social constraints in L2-speaking communities, causing many learners and instructors to seek out ways to reduce foreign accents. Accordingly, research in L2 speech has unceasingly examined various learner-external and learner-internal factors of the occurrence of foreign accents as well as nonnative speech characteristics underlying the judgment of the degree of foreign accents. The current study aimed to expand the understanding of the characteristics and judgments of foreign accents by investigating phonological awareness, a construct pertinent to learners’ phonological knowledge, which has received little attention in research on foreign accents. The current study was exploratory and non-experimental research that targeted 40 adults with Korean-accented English living in the United States. The study first examined how 23 raters speaking American English as their native language detect, perceive, describe, and rate Korean-accented English. Through qualitative and quantitative analyses of the accent perception data, the study identified various phonological and phonetic deviations from the nativelike sounds, which largely result from the influence of first language (Korean) on L2 (English). The study then probed the relationship between foreign accents and learners’ awareness of the phonological system of L2, which was measured using production, perception, and verbalization tasks that tapped into the knowledge of L2 phonology. The study found a significant inverse relationship between the degree of a foreign accent and phonological awareness, particularly implicit knowledge of L2 segmentals. Further in-depth analyses revealed that explicit knowledge of L2 phonology alone was not sufficient for targetlike pronunciation. Findings suggest that L2 speakers experience varying degrees of difficulty in perceiving and producing different L2 segmentals, possibly resulting in foreign-accented speech

Spotting Keywords in Offline Handwritten Documents Using Hausdorff Edit Distance

Keyword spotting has become a crucial topic in handwritten document recognition, by enabling content-based retrieval of scanned documents using search terms. With a query keyword, one can search and index the digitized handwriting which in turn facilitates understanding of manuscripts. Common automated techniques address the keyword spotting problem through statistical representations. Structural representations such as graphs apprehend the complex structure of handwriting. However, they are rarely used, particularly for keyword spotting techniques, due to high computational costs. The graph edit distance, a powerful and versatile method for matching any type of labeled graph, has exponential time complexity to calculate the similarities of graphs. Hence, the use of graph edit distance is constrained to small size graphs. The recently developed Hausdorff edit distance algorithm approximates the graph edit distance with quadratic time complexity by efficiently matching local substructures. This dissertation speculates using Hausdorff edit distance could be a promising alternative to other template-based keyword spotting approaches in term of computational time and accuracy. Accordingly, the core contribution of this thesis is investigation and development of a graph-based keyword spotting technique based on the Hausdorff edit distance algorithm. The high representational power of graphs combined with the efficiency of the Hausdorff edit distance for graph matching achieves remarkable speedup as well as accuracy. In a comprehensive experimental evaluation, we demonstrate the solid performance of the proposed graph-based method when compared with state of the art, both, concerning precision and speed. The second contribution of this thesis is a keyword spotting technique which incorporates dynamic time warping and Hausdorff edit distance approaches. The structural representation of graph-based approach combined with statistical geometric features representation compliments each other in order to provide a more accurate system. The proposed system has been extensively evaluated with four types of handwriting graphs and geometric features vectors on benchmark datasets. The experiments demonstrate a performance boost in which outperforms individual systems

Finite Element Modeling Driven by Health Care and Aerospace Applications

This thesis concerns the development, analysis, and computer implementation of mesh generation algorithms encountered in finite element modeling in health care and aerospace. The finite element method can reduce a continuous system to a discrete idealization that can be solved in the same manner as a discrete system, provided the continuum is discretized into a finite number of simple geometric shapes (e.g., triangles in two dimensions or tetrahedrons in three dimensions). In health care, namely anatomic modeling, a discretization of the biological object is essential to compute tissue deformation for physics-based simulations. This thesis proposes an efficient procedure to convert 3-dimensional imaging data into adaptive lattice-based discretizations of well-shaped tetrahedra or mixed elements (i.e., tetrahedra, pentahedra and hexahedra). This method operates directly on segmented images, thus skipping a surface reconstruction that is required by traditional Computer-Aided Design (CAD)-based meshing techniques and is convoluted, especially in complex anatomic geometries. Our approach utilizes proper mesh gradation and tissue-specific multi-resolution, without sacrificing the fidelity and while maintaining a smooth surface to reflect a certain degree of visual reality. Image-to-mesh conversion can facilitate accurate computational modeling for biomechanical registration of Magnetic Resonance Imaging (MRI) in image-guided neurosurgery. Neuronavigation with deformable registration of preoperative MRI to intraoperative MRI allows the surgeon to view the location of surgical tools relative to the preoperative anatomical (MRI) or functional data (DT-MRI, fMRI), thereby avoiding damage to eloquent areas during tumor resection. This thesis presents a deformable registration framework that utilizes multi-tissue mesh adaptation to map preoperative MRI to intraoperative MRI of patients who have undergone a brain tumor resection. Our enhancements with mesh adaptation improve the accuracy of the registration by more than 5 times compared to rigid and traditional physics-based non-rigid registration, and by more than 4 times compared to publicly available B-Spline interpolation methods. The adaptive framework is parallelized for shared memory multiprocessor architectures. Performance analysis shows that this method could be applied, on average, in less than two minutes, achieving desirable speed for use in a clinical setting. The last part of this thesis focuses on finite element modeling of CAD data. This is an integral part of the design and optimization of components and assemblies in industry. We propose a new parallel mesh generator for efficient tetrahedralization of piecewise linear complex domains in aerospace. CAD-based meshing algorithms typically improve the shape of the elements in a post-processing step due to high complexity and cost of the operations involved. On the contrary, our method optimizes the shape of the elements throughout the generation process to obtain a maximum quality and utilizes high performance computing to reduce the overheads and improve end-user productivity. The proposed mesh generation technique is a combination of Advancing Front type point placement, direct point insertion, and parallel multi-threaded connectivity optimization schemes. The mesh optimization is based on a speculative (optimistic) approach that has been proven to perform well on hardware-shared memory. The experimental evaluation indicates that the high quality and performance attributes of this method see substantial improvement over existing state-of-the-art unstructured grid technology currently incorporated in several commercial systems. The proposed mesh generator will be part of an Extreme-Scale Anisotropic Mesh Generation Environment to meet industries expectations and NASA\u27s CFD visio

THE EFFECT OF STRUCTURE IN SHORT REGIONS OF DNA ON MEASUREMENTS ON SHORT OLIGONUCLEOTIDE MICROARRAY AND ION TORRENT PGM SEQUENCING PLATFORMS

Single-stranded DNA in solution has been studied by biophysicists for many years, as complex structures, both stable and dynamic, form under normal experimental conditions. Stable intra-strand formations affect enzymatic technical processes such as PCR and biological processes such as gene regulation. In the research described here we examined the effect of such structures on two high-throughput genomic assay platforms and whether we could predict the influence of those effects to improve the interpretation of genomic sequencing results. Helical structures in DNA can be composed of interactions across strands or within a strand. Exclusion of the aqueous solvent provides an entropic advantage to more compact structures. Our first experiments were tested whether internal helical regions in one of the two binding partners in a microarray experiment would influence the stability of the complex. Our results are novel and show, from molecular simulations and hybridization experiments, that stable secondary structures on the boundary, when not impinging on the ability of targets to access the probes, stabilize the probe-target hybridization. High-throughput sequencing (HTS) platforms use as templates short single-stranded DNA fragments. We tested the influence of template secondary structure on the fidelity of reads generated using the Ion Torrent PGM platform. It can clearly be seen for targets where hairpin structures are quite long (~20bp) that a high level of mis-calling occurs, particularly of deletions, and that some of these deletions are 20-30 bases long. These deletions are not associated with homopolymers, which are known to cause base mis-calls on the PGM, and the effect of structure on the sequencing reaction, rather than the PCR preparative steps, has not been previously published. As HTS technologies bring the cost of sequencing whole genomes down, a number of unexpected observations have arisen. An example that caught our attention is the prevalence of far more short deletions than had been detected using Sanger methods. The prevalence is particularly high in the Korean genome. Since we showed that helical structures could disrupt the fidelity of base calls on the Ion Torrent we looked at the context of the apparent deletions to determine whether any sequence or structure pattern discriminated them. Starting with the genome provided by Kim et al (1) we selected deletions > 2 bases long from chromosome I of a Korean genome. We created 70 nucleotide fragments centered on the deletion. We simulated the secondary structures using OMP software and then modeled using the Random Forest algorithm in the WEKA modeling package to characterize the relations between the deletions and secondary structures in or around them. After training the model on chromosome I deletions we tested it using chromosome 20 deletions. We show that sequence information alone is not able to predict whether a deletion will occur, while the addition of structural information improves the prediction rates. Classification rates are not yet high: additional data and a more precise structural description are likely needed to train a robust model. We are unable to state which of the structures affect in vitro platforms and which occur in vivo. A comparative genomics approach using 38 genomes recently made available for the CAMDA 2013 competition should provide the necessary information to train separate models if the important features are different in the two cases

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

Dealing with linguistic mismatches for automatic speech recognition

Recent breakthroughs in automatic speech recognition (ASR) have resulted in a word error rate (WER) on par with human transcribers on the English Switchboard benchmark. However, dealing with linguistic mismatches between the training and testing data is still a significant challenge that remains unsolved. Under the monolingual environment, it is well-known that the performance of ASR systems degrades significantly when presented with the speech from speakers with different accents, dialects, and speaking styles than those encountered during system training. Under the multi-lingual environment, ASR systems trained on a source language achieve even worse performance when tested on another target language because of mismatches in terms of the number of phonemes, lexical ambiguity, and power of phonotactic constraints provided by phone-level n-grams. In order to address the issues of linguistic mismatches for current ASR systems, my dissertation investigates both knowledge-gnostic and knowledge-agnostic solutions. In the first part, classic theories relevant to acoustics and articulatory phonetics that present capability of being transferred across a dialect continuum from local dialects to another standardized language are re-visited. Experiments demonstrate the potentials that acoustic correlates in the vicinity of landmarks could help to build a bridge for dealing with mismatches across difference local or global varieties in a dialect continuum. In the second part, we design an end-to-end acoustic modeling approach based on connectionist temporal classification loss and propose to link the training of acoustics and accent altogether in a manner similar to the learning process in human speech perception. This joint model not only performed well on ASR with multiple accents but also boosted accuracies of accent identification task in comparison to separately-trained models