Current Projects

Acoustic representations

A key part of any phonetic study is how acoustic phenomena are represented. This project focuses on quantitative comparisons of the quality of representations and understanding the social and commmunity aspects that influence adoption and choice of acoutic parameters. I also explore novel parameterizations, often drawing on broad acoustic fields.

Data science and pedagogy

Data science methods have become more and more central to social science research, but training in these methods is not yet common practice. I am passionate about helping scientists expand their access to data science and computational methods through formal and informal instruction.

Unsupervised vowel clustering

Machine learning methods show promise for supplementing traditional analyses. This project explores alternatives to the ubiquitous K-means for vowel clustering. Collaboration with Jennifer Kuo (UCLA)

Probing neural net representations

Neural nets have a significant ability to detect patterns in data, and may be informative of the kinds of patterns that are distinctive of phonetic contrasts. This project probes representations at different levels of neural networks.

Recent Updates

Survey Builder in Python

Code for a light-weight command-line program to build and run methodological surveys.

Introduction to bag-of-words and sentiment analysis

Gentle introduction to text analysis and representations for non-major computer science students.

Vowel Clustering Methods

Project exploring OPTICS as an alternative to the common K-Means clustering alorithm for vowel space description. Collaboration with Jennifer Kuo (UCLA)