Files in this item



application/pdfWAGNER-THESIS-2017.pdf (4MB)
(no description provided)PDF


Title:Unsupervised learning of vocal tract sensory-motor synergies
Author(s):Wagner, William Jacob
Advisor(s):Levinson, Stephen E.
Department / Program:Mechanical Sci & Engineering
Discipline:Mechanical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Vocal tract
Sensory-motor primitives
Articulatory speech synthesis
Abstract:The degrees of freedom problem is ubiquitous within motor control arising out of the redundancy inherent in motor systems and raises the question of how control actions are determined when there exist infinitely many ways to perform a task. Speech production is a complex motor control task and suffers from this problem, but it has not drawn the research attention that reaching movements or walking gaits have. Motivated by the use of dimensionality reduction algorithms in learning muscle synergies and perceptual primitives that reflect the structure in biological systems, an approach to learning sensory-motor synergies via dynamic factor analysis for control of a simulated vocal tract is presented here. This framework is shown to mirror the articulatory phonology model of speech production and evidence is provided that articulatory gestures arise from learning an optimal encoding of vocal tract dynamics. Broad phonetic categories are discovered within the low-dimensional factor space indicating that sensory-motor synergies will enable application of reinforcement learning to the problem of speech acquisition.
Issue Date:2017-07-20
Rights Information:Copyright 2017 William Jacob Wagner
Date Available in IDEALS:2017-09-29
Date Deposited:2017-08

This item appears in the following Collection(s)

Item Statistics