Computational modeling of phonetic learning and adaptation using probabilistic inference approaches

Instructors

Connor Mayer	cjmayer@uci.edu
Xin Xie	xxie14@uci.edu

Lecture times

MTWRF

10:30 am-12:00 pm

Lecture locations

MWRF	SBSG 1517
T	SSPB 1222

Course Description

This course will cover computational models that learn linguistic structure from phonetic data using probabilistic inference techniques. In part 1 of the course, taught by Connor Mayer, we will focus on the task of learning vowel categories from acoustic input using Gaussian mixture models and Gibbs sampling. In part 2 of the course, taught by Xin Xie, we will look at models of structured variability in speech, focusing on stop categories. In addition to the technical details of these models, we will discuss how they can serve as useful complementary tools to experimental work.

Course format

Part 1 of the course will be taught in Python using Google Colab.

Part of 2 the course will be taught using R. We recommend that you install RStudio and R Markdown.

Intended audience

We assume basic familiarity with Python, R, or another programming language.

Schedule

8/1	A crash course in phonetics	Code (blank)	Code (solutions)
8/2	Modeling vowel acoustics using Gaussian mixture models	Code (blank)	Code (solutions)
8/3	Learning vowel categories using Gibbs sampling	Code (blank)	Code (solutions)
8/4	Modeling speech perception as probabilistic inference
8/5	Modeling adaptive changes in perception

Resources

Doing Bayesian Data Analysis by John Kruschke
Bayesian inference with tears by Kevin Knight.
Gibbs sampling for the uninitiated by Philip Resnick and Eric Hardisty.
Bayesian Mixture Models and the Gibbs sampler by David M. Blei
The influence of categories on perception: Explaining the perceptual magnet effect as optimal statistical inference by Naomi Feldman, Thomas Griffiths, and James Morgan.
A role for the developing lexicon in phonetic category acquisition by Naomi Feldman, Thomas Griffiths, Sharon Goldwater, and James Morgan.
Hands on programming with R by Garrett Grolemund.
R for Data Science by Hadley Wickham and Garrett Grolemund.
Machine Learning: A Probabilistic Perspective by Kevin Murphy.
Robust Speech Perception: Recognize the Familiar, Generalize to the Similar, and Adapt to the Novel by Dave F. Kleinschmidt and T. Florian Jaeger.