Kyle interviews Zane and Leena about the Endangered Languages Project. My learnings are

  • Project is taking in 3.5 hours of audio content from an endangered language called “Ladin”
  • It creates phonetic transcriptions from audio samples of human languages
  • Model has so far produced decent levels of vowel identifications
  • Currently working on phoneme segmentation and larger consonant categories
  • From the project blurb

In this project, we are trying to speed up the process of language documentation by building a model that produces phonetic transcriptions from audio samples of human languages. The ultimate goal of our project is to develop a model that could be applied to any human language with minimal changes. We will be using around 3-4 hours of partially labeled audio data in an endangered language called Ladin, which we are using as our main training/test data. As of now we have produced some decent results in vowel identifications and are currently working on phoneme segmentation and identification of larger consonant categories.

  • Other projects links are at are
    • ML Fairness project
    • NLP based on AirBNB: This project will utilize NLP to analyze the correlation between Airbnb reviews and gentrification in neighborhoods. In particular, we will attempt to predict crime rates, race/income diversity, house prices, and other similar statistics from Airbnb reviews in a particular geographical region. This is advantageous because it provides a detailed local picture and real time statistics about a neighborhood compared to years old government data. This project builds on a recent Harvard study that used Yelp data to predict economic opportunity in neighborhoods, and can expand to include other consumer based data as well.
    • Code to English:An integral step to learning how to code is being able to decipher the meaning of a code block. In this project we aim to use pairs of python questions from StackOverflow and code from their accepted answers to try and build a model that will generate an English description given a block of code. This project will begin by building the data set by writing a crawler to grab the code/description pairs from StackOverflow. We will then utilize NLP and GANs/VAEs to decompose code into a latent space, transfer between the code and description latent spaces, and then recompose the description from the latent space. The intended outcome of this project is to roll out a tool to allow beginners to understand what their code is doing, potentially as a website. As with any non-vetted data set, this project is an experiment and may fail. However, it will teach valuable skills across the ML project life cycle, from data gathering to writing testing suites to building the actual models.