## ASU Electronic Theses and Dissertations

This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

Contributor
Status
• Public
Date Range
2013 2019

## Recent Submissions

Eigenvalues of the Gram matrix formed from received data frequently appear in sufficient detection statistics for multi-channel detection with Generalized Likelihood Ratio (GLRT) and Bayesian tests. In a frequently presented model for passive radar, in which the null hypothesis is that the channels are independent and contain only complex white Gaussian noise and the alternative hypothesis is that the channels contain a common rank-one signal in the mean, the GLRT statistic is the largest eigenvalue $\lambda_1$ of the Gram matrix formed from data. This Gram matrix has a Wishart distribution. Although exact expressions for the distribution of $\lambda_1$ are known …

Contributors
Jones, Scott, Cochran, Douglas, Berisha, Visar, et al.
Created Date
2019

Dementia is a syndrome resulting from an acquired brain disease that affects many domains of cognitive impairment. The progressive disorder generally affects memory, attention, executive functions, communication, and other cognitive domains that significantly alter everyday function (Quinn, 2014). The purpose of this research was to gather a systematic review of cognitive-communication assessments and screeners used in assessing dementia to assist in early prognosis. From this review, there is potential in developing a new test to address the areas that people with dementia often have deficits in 1) Memory, 2) Attention, 3) Executive Functions, 4) Language, and 5) Visuospatial Skills. In …

Contributors
Miller, Marissa, Liss, Julie M, Berisha, Visar, et al.
Created Date
2019

Speech intelligibility measures how much a speaker can be understood by a listener. Traditional measures of intelligibility, such as word accuracy, are not sufficient to reveal the reasons of intelligibility degradation. This dissertation investigates the underlying sources of intelligibility degradations from both perspectives of the speaker and the listener. Segmental phoneme errors and suprasegmental lexical boundary errors are developed to reveal the perceptual strategies of the listener. A comprehensive set of automated acoustic measures are developed to quantify variations in the acoustic signal from three perceptual aspects, including articulation, prosody, and vocal quality. The developed measures have been validated on …

Contributors
Jiao, Yishan, Berisha, Visar, Liss, Julie, et al.
Created Date
2019

In recent years, conventional convolutional neural network (CNN) has achieved outstanding performance in image and speech processing applications. Unfortunately, the pooling operation in CNN ignores important spatial information which is an important attribute in many applications. The recently proposed capsule network retains spatial information and improves the capabilities of traditional CNN. It uses capsules to describe features in multiple dimensions and dynamic routing to increase the statistical stability of the network. In this work, we first use capsule network for overlapping digit recognition problem. We evaluate the performance of the network with respect to recognition accuracy, convergence and training time …

Contributors
XIONG, YAN, Chakrabarti, Chaitali, Berisha, Visar, et al.
Created Date
2018

Motion estimation is a core task in computer vision and many applications utilize optical flow methods as fundamental tools to analyze motion in images and videos. Optical flow is the apparent motion of objects in image sequences that results from relative motion between the objects and the imaging perspective. Today, optical flow fields are utilized to solve problems in various areas such as object detection and tracking, interpolation, visual odometry, etc. In this dissertation, three problems from different areas of computer vision and the solutions that make use of modified optical flow methods are explained. The contributions of this dissertation …

Contributors
Kanberoglu, Berkay, Frakes, David, Turaga, Pavan, et al.
Created Date
2018

With advances in automatic speech recognition, spoken dialogue systems are assuming increasingly social roles. There is a growing need for these systems to be socially responsive, capable of building rapport with users. In human-human interactions, rapport is critical to patient-doctor communication, conflict resolution, educational interactions, and social engagement. Rapport between people promotes successful collaboration, motivation, and task success. Dialogue systems which can build rapport with their user may produce similar effects, personalizing interactions to create better outcomes. This dissertation focuses on how dialogue systems can build rapport utilizing acoustic-prosodic entrainment. Acoustic-prosodic entrainment occurs when individuals adapt their acoustic-prosodic features of …

Contributors
Lubold, Nichola Anne, Walker, Erin, Pon-Barry, Heather, et al.
Created Date
2018

Much evidence has shown that first language (L1) plays an important role in the formation of L2 phonological system during second language (L2) learning process. This combines with the fact that different L1s have distinct phonological patterns to indicate the diverse L2 speech learning outcomes for speakers from different L1 backgrounds. This dissertation hypothesizes that phonological distances between accented speech and speakers' L1 speech are also correlated with perceived accentedness, and the correlations are negative for some phonological properties. Moreover, contrastive phonological distinctions between L1s and L2 will manifest themselves in the accented speech produced by speaker from these L1s. …

Contributors
Tu, Ming, Berisha, Visar, Liss, Julie M, et al.
Created Date
2018

A human communications research project at Arizona State University aurally recorded the daily interactions of aware and consenting employees and their visiting clients at the Software Factory, a software engineering consulting team, over a three year period. The resulting dataset contains valuable insights on the communication networks that the participants formed however it is far too vast to be processed manually by researchers. In this work, digital signal processing techniques are employed to develop a software toolkit that can aid in estimating the observable networks contained in the Software Factory recordings. A four-step process is employed that starts with parsing …

Contributors
Pressler, Daniel, Bliss, Daniel W, Berisha, Visar, et al.
Created Date
2018

Machine learning (ML) has played an important role in several modern technological innovations and has become an important tool for researchers in various fields of interest. Besides engineering, ML techniques have started to spread across various departments of study, like health-care, medicine, diagnostics, social science, finance, economics etc. These techniques require data to train the algorithms and model a complex system and make predictions based on that model. Due to development of sophisticated sensors it has become easier to collect large volumes of data which is used to make necessary hypotheses using ML. The promising results obtained using ML have …

Contributors
Dutta, Arindam, Bliss, Daniel W, Berisha, Visar, et al.
Created Date
2018

This dissertation is focused on developing an algorithm to provide current state estimation and future state predictions for biomechanical human walking features. The goal is to develop a system which is capable of evaluating the current action a subject is taking while walking and then use this to predict the future states of biomechanical features. This work focuses on the exploration and analysis of Interaction Primitives (Amor er al, 2014) and their relevance to biomechanical prediction for human walking. Built on the framework of Probabilistic Movement Primitives, Interaction Primitives utilize an EKF SLAM algorithm to localize and map a distribution …

Contributors
Clark, Geoffrey Mitchell, Ben Amor, Heni, Si, Jennie, et al.
Created Date
2018