Will Styler's Homepage
Will Styler

Associate Teaching Professor of Linguistics at UC San Diego

Director of UCSD's Computational Social Science Program

Curriculum Vitae and Publications

Here’s my latest Curriculum Vitae, with links to all PDF versions and captured lectures. You can also download this document as a PDF.




Basic Information

Current Positions

Past Positions

Education

Research Interests

Honors and Awards

Publications

Below are my current publications. To see my citations in other people’s work, visit my Google Scholar Page.

Peer-Reviewed

Non-Peer-Reviewed

Satire and Fiction

Conference Presentations and Posters

See my posters page for full-size PDFs of all posters I’ve presented at conferences.

Invited Talks

Teaching Experience

Instructor, as Teaching Faculty at UC San Diego

Legend: F=Fall Quarter, W=Winter Quarter, S=Spring Quarter, AY=All three quarters in the given Academic Year, with the number being the 20__ year.

Instructor for Independent Study

Workshop Instructor

Invited Panelist

Lead Graduate Teacher for the University of Colorado Department of Linguistics

Graduate Instructor-of-Record, University of Colorado at Boulder

Chair and Coordinator, 2007 CU Stampede First Year Leadership Camp

Invited Guest Lecturer

Academic Service

University Service at UC San Diego

Student Advising

Doctoral Dissertation Co-Advisor

Doctoral Dissertation Committee Member

Honors Thesis Advisor

Honors Thesis Committee Member

CSS Capstone Mentor

Reviewer for Journals and Conferences

Corpora Maintained

Competencies

Human languages

Technological Competencies

Doctoral Dissertation: ‘On the Acoustical and Perceptual Features of Vowel Nasality’

Overview

Vowel nasality is, simply put, the difference in the vowel sound between the English words “pat” and “pant”, or between the French “beau” and “bon”. This phenomenon is used in languages around the world, but is relatively poorly understood from an acoustical standpoint, meaning that although we as human listeners can easily hear that a vowel is or isn’t nasalized, it’s quite difficult for us to measure or identify that nasality in a laboratory context.

The goal of my dissertation is to better understand vowel nasality in language by discovering not just what parts of the sound signal change in oral vs. nasal vowels, but which parts of the signal are actually used by listeners to perceive differences in nasality.

I’ve written up a summary of the process, aimed at a more general audience, here, or you can read the abstract below.

Dissertation 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 L1 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.

Dissertation Details

Past Research Positions

University of Michigan, Department of Linguistics

University of Colorado at Boulder, Department of Linguistics

Volunteer Work

Work for the CU Boulder Residence Hall Association

Student Leadership Mentor, CU Stampede First Year Leadership Camp

Invited Program Presenter, CU Stampede First Year Leadership Camp

Winner, National Residence Hall Honorary CU Student of the Month

Version History