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code
kaldi
partitions
README.md

README.md

Neural Acoustic Word Embeddings for Switchboard

Overview:

This is a recipe for learning neural acoustic word embeddings for a subset of Switchboard. The models are explained in greater detail in Settle & Livescu, 2016 as well as Settle et al., 2017:

  • S. Settle and K. Livescu, "Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches," in Proc. SLT, 2016.
  • S. Settle, K. Levin, H. Kamper, and K. Livescu, "Query-by-Example Search with Discriminative Neural Acoustic Word Embeddings," in Proc. Interspeech, 2017.

Contents:

code/

  • python code to create, run, and save the model

kaldi/

  • modification of Kaldi's swbd/s5c used to extract features and set up data as required
  • word/segment list used in prior work Kamper et al., 2015 can also be found under kaldi/data/kamperh

partitions/

  • train/dev/test partitioning into Switchboard conversation sides (these are consistent with prior work, and extracted from the aforementioned word/segment list)

Steps:

  1. Ensure access to installed dependencies.

  2. Clone repo.

  3. Check that $KALDI_ROOT variable points to the location of installed/compiled kaldi. This can be set in your ~/.bashrc or in kaldi/path.sh.

  4. Update kaldi/run.sh:

    • set $swbd variable to your local switchboard datapath
    • set $nj to number of desired jobs (default=8)
    • set $stage to desired stage in feature creation (default=1)
    • set $min_word_length to desired minimum length character sequence allowed for included words (default=6)
    • set $min_audio_duration to minimum audio duration (in frames) allowed for included audio (default=50)
    • set $min_train_occurrence_count to limit how common training words must have been (default=2, note: this must be >= 2 or siamese training will not work)
  5. Navigate to kaldi directory and run "./run.sh". Now you should have the desired features.

  6. Navigate to code directory and run "python main.py". This will train, evaluate, and save models.