Johns Hopkins University
Hi! This summer I have three jobs! I work for the Linguistic and Music departments of Macalester College, doing odd jobs and preparing for next year. I also teach topics like AI/ML, app development, and 3D printing to kids for iD Tech Camps.
At the end of summer I'm moving to Baltimore to start a PhD in cognitive science. If anyone has any thoughts on apartment hunting there please do let me know!!!!!
My research tends to probe potential interactions between articulatory characteristics of speech and sociological factors by acoustic analysis with machine learning.
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Johns Hopkins UniversityDepartment of Cognitive Science
Ph.D. StudentSep. 2026 - present -
Macalester CollegeB.A. in Linguistics and Computer ScienceSep. 2022 - May 2026
Honors & Awards
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The Linguistics Prize for Academic ExcellenceMacalester College Linguistics Department2026
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The "Weakly Autonomous" AwardMacalester College Computer Science Department2026
Selected Publications (view all )
A Comparative Analysis of Acoustic and Electroglottographic Measures: Insights from Vocal Fold Vibratory Patterns
Henry Heyden
Linguistics Honors Projects 2026
Speakers of languages with phonation contrasts produce voice qualities like "creaky" or "breathy" in different ways. In this project, I used functional principal component analysis to investigate the dominant dimensions of variation in electroglottographic data, in an attempt to further cross-linguistic understanding of non-modal phonation production.
A Comparative Analysis of Acoustic and Electroglottographic Measures: Insights from Vocal Fold Vibratory Patterns
Henry Heyden
Linguistics Honors Projects 2026
Speakers of languages with phonation contrasts produce voice qualities like "creaky" or "breathy" in different ways. In this project, I used functional principal component analysis to investigate the dominant dimensions of variation in electroglottographic data, in an attempt to further cross-linguistic understanding of non-modal phonation production.
Phonetically-Motivated Feature Engineering for Automatic Genre Classification
Capstone Project for Linguistics Major at Macalester College. 2026
Boundaries between musical boundaries are fuzzy, but certain aspects of vocal performance can effectively index genre (e.g., vibrato in Opera or “rasp” in Country). To probe the salience of these vocal characteristics, I trained a machine learning classifier to predict the genre of a song based solely on acoustic measurements of the lead vocal. A support vector machine reached 45% accuracy on unseen data, and confusion matrices suggested that some genres had more salient voice quality characteristics than others.
Phonetically-Motivated Feature Engineering for Automatic Genre Classification
Capstone Project for Linguistics Major at Macalester College. 2026
Boundaries between musical boundaries are fuzzy, but certain aspects of vocal performance can effectively index genre (e.g., vibrato in Opera or “rasp” in Country). To probe the salience of these vocal characteristics, I trained a machine learning classifier to predict the genre of a song based solely on acoustic measurements of the lead vocal. A support vector machine reached 45% accuracy on unseen data, and confusion matrices suggested that some genres had more salient voice quality characteristics than others.