Mark when you've finished them.

• (Kyle) Preprocessor caching ✅
• (Kyle) Preprocessor returns categorical distribution ✅
• (Kyle) Embedding baseline
• (Seong) Python scripts (not notebooks) that use grid search, mag and save the plots in /visualizations for the following models: ✅
  • Random Forest
  • XGBoost
  • Gaussian Discriminant Analysis
  • Naive Bayes
  • Logistic Regression
  • Principal Component Analysis
  • Support Vector Machine
  • K-Nearest Neighbors
  • K-Means
  • Gaussian Mixture Model
• (Jared) Semi-supervised learning scripts (not notebooks) with fully-connected layer and 1-D CNN
  • Self-training
  • Co-training
  • Pi-model
  • Label propagation
  • Label gradient alignment
  • Using your model against itself
• (anyone)

Directory Structure

• embedding/: Learned embeddings, applied after preprocessing. For example, PCA.
• experiments/class/: mag experiments on classification (phoneme boundaries given to model).
• experiments/seg_class/: mag experiments on segmentation and classification (phoneme boundaries produced by model).
• models/: custom model classes we've built.
• preprocessing/: Loads data from files, caches it, and returns NumPy arrays.
• results/: Assorted images/ plots that are interesting and could be useful in the final report. For example, a PCA .png
• temp_{jared, kyle, seong}/: The equivalent of branches. Put work-in-progress here, and bring it out into the main system when it's done.
• visualizations/: Examples of how to plot a Mel spectrogram, etc.

Testing

• Run pytest test_main.py.
• Add additional tests there. We'll use a single test module for now. pytest uses simple assert statements.

Rules

• The directory containing speech2phone must be on the environment variable PYTHONPATH.
• To append it, run export PYTHONPATH="${PYTHONPATH}:/my/other/path".
• For example, if I have Users/jarednielsen/Desktop/speech2phone, then I must have Users/jarednielsen/Desktop on my PYTHONPATH.
• If that doesn't work because of conda, Add a .pth file to the directory $HOME/path/to/anaconda/lib/pythonX.X/site-packages. This can be named anything (it just must end with .pth). A .pth file is just a newline-separated listing of the full path-names of directories that will be added to your path on Python startup. For example, /anaconda3/envs/py36/lib/python3.6/site-packages/path.pth has the line /Users/jarednielsen/Desktop in it.
• Use absolute imports everywhere. For example, import speech2phone or import speech2phone.preprocessing.
• See speech2phone/__init__.py and speech2phone/preprocessing/__init__.py for examples of how to set up subpackages.
• /preprocessing applies classic data processing methods (i.e. not learned) to the data, while /embedding applies learned methods. For example, Mel spectrogram stuff should be handled in /preprocessing.

boundary recognition

Approaches

• Recurrent network
• Merging (like piecewise linear regression) with the criterion over a metric using dynamic time-warping

/embedding

Options for embedding include:

• spectrum
• cepstrum
• single linear layer (we could try this or just SGD)
• more complex learned network
• autoencoder
• UMAP
• t-SNE

These will all be specifiable by importing from the embedding module. The spectrum works pretty well as an embedding space, as we found by doing some PCA (see /visualizations/pca_embedding.png). I think we'll use it as a baseline.

Things to try (add ideas here)

• trinemes
• dynamic time-warping
• reapply models to TIMIT to quantify results (quantified semi-supervised learning)
• use Mel spectrogram but give some time dependence (80 freq x 10 time)
• using the activations from the neural network to try to predict the speaker, and then consider the ethical implications (a la "voiceprint" technology)