Computational Linguistics Commons^™

Exploring Asynchronous Pronunciation Training Through Context-Aware Pronunciation Applications, Claire L. Schweikert

Theses/Capstones/Creative Projects

This paper provides a survey of various research articles on context-aware asynchronous pronunciation training applications. First, a set of seven articles is reviewed and summarized. Next, they are synthesized over the three main topics of 1) automated speech recognition, 2) non-native speaker considerations in language learning, and 3) future directions for research and development within computer-assisted pronunciation training (CAPT). Research in the areas of acoustic and pronunciation modeling (both implicit and explicit), pedagogical considerations for CAPT application design, Goodness of Pronunciation algorithm scoring, accent recognition and neutralization, and more are discussed.

Single-Case Pilot Study For Longitudinal Analysis Of Referential Failures And Sentiment In Schizophrenic Speech From Client-Centered Psychotherapy Recordings, Travis A. Musich

Dissertations

Though computational linguistic analyses have revealed the presence of distinctly characteristic language features in schizophrenic disordered speech, the relative stability of these language features in longitudinal samples is still unknown. This longitudinal pilot study analyzed schizophrenic disordered speech data from the archival therapy audio recordings of one patient spanning 23 years. End-to-end Neural Coreference Resolution software was used to analyze transcribed speech data from three therapy sessions to identify ambiguous pronouns, referred to as referential failures, which were reviewed and confirmed by multiple raters. Speech samples were analyzed using Google Cloud Natural Language API software for sentiment variables (i.e., score, …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Data-Driven Neuroanatomical Subtypes In Various Stages Of Schizophrenia: Linking Cortical Thickness, Glutamate, And Language Functioning, Liangbing Liang

Electronic Thesis and Dissertation Repository

The considerable variation in the spatial distribution of cortical thickness changes has been used to parse heterogeneity in schizophrenia. We aimed to recover a ‘cortical impoverishment’ subgroup with widespread cortical thinning. We applied hierarchical cluster analysis to cortical thickness data of three datasets in different stages of psychosis and studied the cognitive, functional, neurochemical, language and symptom profiles of the observed subgroups. Our consensus-based clustering procedure consistently produced a subgroup characterized by significantly lower cortical thickness. This ‘cortical impoverishment’ subgroup was associated with a higher symptom burden in a clinically stable sample and higher glutamate levels with language impairments in …

Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian

Northeast Journal of Complex Systems (NEJCS)

In this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection consists of interviews conducted by experts …

Beneath The Surface Of Talking About Physicians: A Statistical Model Of Language For Patient Experience Comments, Taylor Turpen, Lea Matthews Md, Senem Guney Phd, Cpxp

Patient Experience Journal

This study applies natural language processing (NLP) techniques to patient experience comments. Our goal was to examine the language describing care experiences with two groups of physicians: those with scores in the top 100 and those with scores in the bottom 100 among all physicians (n=498) who received scores from patient satisfaction surveys. Our analysis showed a statistically significant difference in the language used to describe care experiences with these two distinct groups of physicians. This analysis illustrates how to apply NLP techniques in categorizing and building a statistical model for language use in order to identify meaningful language and …

Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang

COBRA Preprint Series

Non-negative matrix factorization (NMF) is a widely used machine learning algorithm for dimension reduction of large-scale data. It has found successful applications in a variety of fields such as computational biology, neuroscience, natural language processing, information retrieval, image processing and speech recognition. In bioinformatics, for example, it has been used to extract patterns and profiles from genomic and text-mining data as well as in protein sequence and structure analysis. While the scientific performance of NMF is very promising in dealing with high dimensional data sets and complex data structures, its computational cost is high and sometimes could be critical for …

The Use Of Gesture In Self-Initiated Self-Repair Sequences By Persons With Non-Fluent Aphasia, Eleanor M. Feltner

Theses and Dissertations--Linguistics

This study examines the relationship between types of gestures and instances of self-initiated self-repair (SISR) used by persons with non-fluent aphasia (NFA), which is a type of aphasia characterized by stilted speech or signing (Papathanasiou et al., 2013), in interactions with clinicians. Conversation repairs in this study are assessed using the framework of Conversation Analysis (CA), which is an approach for describing, analyzing, and understanding social interaction (Sidnell, 2010). Previous linguistic studies have demonstrated a distinct preference for the use of gesture during a repair by persons with aphasia (Goodwin, 1995; Klippi, 2015; Wilkinson, 2013). This study draws more conclusive …

A Comparison Of Norm-Referenced, Traditional, And Computer-Assisted Language Assessments, Michel P. Helmke

Masters Theses

Current literature in the field of communication disorders suggests that traditional norm-referenced tests may yield erroneous or misleading information regarding a child's level of language acquisition. Additional research suggests that the most valid and reliable technique for determining a client's level of linguistic expertise is language sampling and analysis. Language sampling and analysis has traditionally been rejected as a means of evaluation, especially for the school-age child, due to the length of time necessary to complete such analyses. In recent years, language sampling and analysis techniques have been redesigned as computer software application programs. Computer software application programs may significantly …