This is a recipe for extracting acoustic word embeddings for a subset of the Switchboard corpus. The models are described in detail in Kamper et al., 2015:

• H. Kamper, W. Wang, and K. Livescu, "Deep convolutional acoustic word embeddings using word-pair side information," in Proc. ICASSP, 2016.

Please cite this paper if you use this code. All the neural networks are implemented in the package couscous.

1. Install all dependencies (below).

2. Clone couscous into the appropriate directory:

   mkdir ../src
   git clone https://github.com/kamperh/couscous.git ../src/couscous
   

3. Run the steps in kaldi_features/run.sh.

4. Run the steps in cnn_wordembeds/readme.md.

5. If you run the steps correctly above, then if you execute the following:

   cd cnn_wordembeds/
   ./apply_layers.py models/siamese_triplets_cnn.1/ test
   ./eval_samediff.py \
       models/siamese_triplets_cnn.1/swbd.test.layer_-1.npz
   

   Then the evaluation should show the following output:

   Average precision: 0.537404372048
   Precision-recall breakeven: 0.542724052097
   

   The average precision (AP) of 0.537 is used for the number reported in Table 1, row 9 of Kamper et al., 2015.

• Kaldi
• Theano and all its dependencies.
• couscous: should be cloned into the directory ../src/couscous.

• Herman Kamper
• Weiran Wang
• Karen Livescu