Date of Award

Spring 3-24-2015

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Linguistics

First Advisor

Rebecca Scarborough

Second Advisor

Kathy Arehart

Third Advisor

Mans Hulden

Abstract

Although much is known about the linguistic function of vowel nasality, either contrastive (as in French) or coarticulatory (as in English), less is known about its perception. This study uses careful examination of production patterns, along with data from both machine learning and human listeners to establish which acoustical features are useful (and used) for identifying vowel nasality.

A corpus of 4,778 oral and nasal or nasalized vowels in English and French was collected, and feature data for 29 potential perceptual features was extracted. A series of Linear Mixed-Effects Regressions showed 7 promising features with large oral-to-nasal feature differences, and highlighted some cross-linguistic differences in the relative importance of these features. Two machine learning algorithms, Support Vector Machines and RandomForests, were trained on this data to identify features or feature groupings that were most effective at predicting nasality token-by-token in each language. The list of promising features was thus narrowed to four: A1-P0, Vowel Duration, Spectral Tilt, and Formant Frequency/Bandwidth.

These four features were manipulated in vowels in oral and nasal contexts in English, adding nasal features to oral vowels and reducing nasal features in nasalized vowels, in an attempt to influence oral/nasal classification. These stimuli were presented to native English listeners in a lexical choice task with phoneme masking, measuring oral/nasal classification accuracy and reaction time. Only modifications to vowel formant structure caused any perceptual change for listeners, resulting in increased reaction times, as well as increased oral/nasal confusion in the oral-to-nasal (feature addition) stimuli. Classification of already-nasal vowels was not affected by any modifications, suggesting a perceptual role for other acoustical characteristics alongside nasality-specific cues. A Support Vector Machine trained on the same stimuli showed a similar pattern of sensitivity to the experimental modifications.

Thus, based on both the machine learning and human perception results, formant structure, particularly F1 bandwidth, appears to be the primary cue to the perception of nasality in English. This close relationship of nasal- and oral-cavity derived acoustical cues leads to a strong perceptual role for both the oral and nasal aspects of nasal vowels.

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