Clustering of the Hillenbrand vowel data

This repo contains Jupyter/IPython notebooks and Python and R scripts using the data from Hillenbrand et al. 1995 on the acoustic measurements on American English vowels. The vowels are either identified using a Support Vector Machine or separated into different clusters using k-means clustering and Gaussian mixture models. Learning algorithms are implemented in scikit-learn and visualization is performed in R using ggplot2 (clusters only). For the supervised learning, formant values are used as predictors; for the cluster analyses, formants and formant ratios are used as the features for the clustering.

Steps:

1. Import data and remove rows with at least one missing observation
2. Identify observation by word, vowel, and sex of speaker
3. Map vowel characteristics to observation (e.g., front, open-mid, etc.)
4. Create targets for speaker sex, word, and vowel (for supervised learning)
5. Normalizing features (z-score)
6. Create feature matrices
7. Implement classification or clustering algorithms
8. Visualize clusters and feature space

Directories and contents

data:

• Vowel measurement data in CSV format
• Vowel observations and cluster assignments for both k-means and Gaussian mixture models

notebooks:

• Jupyter/IPython notebook using Python kernel to format data for unsupervised learning
• Jupyter/IPython notebook using Python kernel to classify vowels using a Support Vector Machine
• Jupyter/IPython notebook using Python kernel to implement clustering of vowel observations
• Jupyter/IPython notebooks using R kernel to plot the data

scripts:

• Python script to format data for unsupervised learning
• Python script to classify vowels using a Support Vector Machine
• Python script to implement clustering of vowel observations
• R scripts to plot the data