All development was done in Python 2.7

scipy==1.0.0
numpy==1.11.0
Keras==2.2.4
pandas==0.22.0
Theano==1.0.4
matplotlib==2.2.2
resampy==0.2.0
tqdm==4.11.2
scikit_learn==0.20.3
utils==0.9.0

We propose a novel direction of multiview learning to obtain speech representations in presence of multiple known sources of variability. We constrain one mode of variability as multiple views and learn features that are discriminative in the other mode. A schematic to the approach is shown below:

1. Download Speech Commands Dataset as follows:

cd ./data && wget http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz && tar -xvzf speech_commands_v0.01.tar.gz

2. Extract log-mel features (using defaults params in ./audioset_scripts/vggish_params.py; edit these to replicate experiments in the paper)

python extract_logmel_features.py

3. Learn speaker-invariant representations of speech commands for Command-ID task

python train_speech_commands.py

4. Learn command-invariant representations of speakers for Speaker-ID task

python train_speaker_embeddings_open_set.py

5. Do inference after the model is trained; See example inference code:

python test_speaker_embeddings_open_set.py

This last script should generate SVM predictions, as well as t-SNE embeddings for the shared representations learnt