Computational Linguistics Commons^™

Phonologically-Informed Speech Coding For Automatic Speech Recognition-Based Foreign Language Pronunciation Training, Anthony J. Vicario

Dissertations, Theses, and Capstone Projects

Automatic speech recognition (ASR) and computer-assisted pronunciation training (CAPT) systems used in foreign-language educational contexts are often not developed with the specific task of second-language acquisition in mind. Systems that are built for this task are often excessively targeted to one native language (L1) or a single phonemic contrast and are therefore burdensome to train. Current algorithms have been shown to provide erroneous feedback to learners and show inconsistencies between human and computer perception. These discrepancies have thus far hindered more extensive application of ASR in educational systems.

This thesis reviews the computational models of the human perception of American …

Quantifying Coherence In A Transdiagnostic Sample: A Methodological Investigation Of Computationally-Derived Coherence Using Ambulatory Assessment, Taylor L. Fedechko

LSU Master's Theses

Schizophrenia is a clinical diagnosis assigned to individuals that experience positive (e.g., hallucinations and delusions), negative (e.g., blunted affect), and disorganized (e.g., incoherent speech) symptoms. One particularly disabling symptom is incoherence, which is defined as the meaning-based relationship between ideas. This symptom can drastically affect an individual’s quality of life by affecting areas such as social and occupational functioning. Currently, the mechanism behind this symptom is unknown and requires further study. One way to examine incoherence is to understand its level of expression in other clinical populations. With the advent of computationally-derived natural language processing (NLP), coherence can be quantified …

A Study On The Efficacy Of Sentiment Analysis In Author Attribution, Michael J. Schneider

Electronic Theses and Dissertations

The field of authorship attribution seeks to characterize an author’s writing style well enough to determine whether he or she has written a text of interest. One subfield of authorship attribution, stylometry, seeks to find the necessary literary attributes to quantify an author’s writing style. The research presented here sought to determine the efficacy of sentiment analysis as a new stylometric feature, by comparing its performance in attributing authorship against the performance of traditional stylometric features. Experimentation, with a corpus of sci-fi texts, found sentiment analysis to have a much lower performance in assigning authorship than the traditional stylometric features.