Comparing Human Perception to Computational Classifications of Lexical Tones

This dissertation analyzed the tonal and acoustic properties of utterances produced from five native Thai speakers. The computational model produced classifications based on predictions made by a Hidden Markov Model that simulates tone perception and categorization. The computational model tested the categorization of stimuli taken from both citation and continuous contexts of Thai tonal data, in order to compare the performance of the computational model on both clear and naturalistic stimuli. Two perception experiments were also conducted, involving human listeners, for the purpose of comparing their behavior to that of the computational model. The results reveal that the classifications of lexical tone categories made by the computational model yield some dissimilar learning patterns to that found in human perceptual learning of the same categories.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Discipline thesis:degree_discipline

Linguistics

Grantor dc:publisher.institution

Graduate Studies

Year dc:date.issued

2015

Author dc:creator

• Cooper-Leavitt, Jamison

Advisor dc:contributor.advisor

• Winters, Stephen

• Linguistics

Statement dc:rights

• University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.

Language dc:language.iso

eng

DOI

http://dx.doi.org/10.11575/PRISM/25369

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:11023/2029