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Zero-resource speech utils

This repository contains documentation and utility scripts for several zero-resource speech systems, notably the correspondence autoencoder (cAE) and the ZRTools unsupervised term discovery (UTD) system. The main focus is on using them with the GlobalPhone corpus. This is not a detailed tutorial and mainly gives high-level instructions. While these allow to run most things, it is expected you also become familiar with the respective systems.

Because of dependencies on a number of different systems, it might not be straightforward to run all of these scripts. Most dependencies are forks because of modifications I made. See the individual repositories for detailed installation instructions and further dependencies. Most dependencies expect Python 2.

Adjust the config file as necessary for your local setup.

Contents

Acoustic feature extraction, forced alignment

Dependencies

Acoustic feature extraction

. config
cd $KALDI_GP

# Prepare the data for the specified languages.
./prepare_data.sh "FR SP"

# Extract acoustic features for the specified languages.
./make_feats.sh "FR SP" mfcc

VTLN

VTLN was found to be very beneficial for the cAE and UTD [1]. The following script trains VTLN models and extracts adapted features (languages specified in the script).

. config
cd $KALDI_GP

# Train VTLN model and extract VTLN-adapted MFCCs.
./train_lvtln.sh 0 mfcc

Forced alignment

The following script trains a basic context-dependent triphone model which is used for forced alignment (languages specified in the script). The later stages also train more advanced models and extract high-resolution MFCCs for DNN training, which can all be skipped if only forced alignments are needed. The output will be word- and phone-level alignments in .ctm format and is stored in the folder specified in $KALDI_GP/path.sh.

. config
cd $KALDI_GP

# Train monolingual acoustic models, do forced alignment.
./train_monolingual.sh 0

Convert Kaldi .ark to Numpy .npz/.npy format

The other systems generally expect features in Numpy format.

. config

# Convert .ark to .npz
./kaldi/ark2npz.sh $KALDI_GP/data/SP/train_mfcc $DATA/SP/train_mfcc.npz

# Convert .npz to .npy (if necessary)
./misc/npz_to_npy.py $DATA/SP/train_mfcc.npz $DATA/SP/train_mfcc.npy

cAE training

See the following repository for detailed instructions to pre-train and train the cAE and to use it to encode features. Pre-training only requires a .npy archive of speech features, cAE training requires DTW-aligned word pairs (see below).

Note: For this you have to install Theano version 0.9 (pip install theano==0.9) to be able to use Pylearn2.

Word pairs

Gold-standard pairs

Gold-standard same-word pairs are extracted from the force-aligned transcriptions. Words that are at least 0.5 seconds and 5 characters long are selected and then all possible same-word pairs are generated. For the training set, the maximum number of pairs to generate per word type can be set to limit the total number of pairs.

The script also generates word pairs (both same-word and different-word) for the same-different evaluation task (see below).

. config

# Extract gold-standard pairs for French and Spanish with no more than 20 pairs
# per word type.
./word_pairs/extract_pairs.sh "FR SP" 20

UTD pairs

Dependencies

In a true zero-resource setting, the word pairs are obtained from an unsupervised term discovery (UTD) system, in our case ZRTools. Note: there are currently several directories set by hand within the ZRTools scripts, so the following instructions won't work out-of-the-box on a different system.

. config
cd $ZRTOOLS

# Run UTD on the Spanish training data over 30 cores.
./run_utd SP 30 train

# Check progress of UTD
./done.sh exp/gp-SP

# Cluster UTD matches with DTW threshold 0.8
# I always do this initially so that information about all matches is
# preserved in the `matches/master_match` file and I can delete files
# in the next step that can take up a lot of space.
./post_disc gp-SP 0.8 SP

# Delete files that take up space and aren't needed anymore.
rm exp/gp-SP/disc.cmd*
rm exp/gp-SP/matches/out*

# Now do the actual clustering from which we will generate pairs. You might
# want to fine-tune the threshold, but 0.88 works well for PLPs.
./post_disc gp-SP 0.88 SP

# Go back.
cd $OLDPWD

After clustering and extracting the pairs, a few more steps are required to prepare them for cAE training.

. config

# Post-process the pairs.
./utd/postprocess_pairs.sh exp/gp-SP SP

# The previous script conveniently prints the steps you should run next:

Evaluation

Same-different

ABX

UTD evaluation

Dependencies

  • ABXpy
  • bucktsong_segmentalist
  • speech_dtw
  • tde

References

[1] E. Hermann, S. Goldwater, "Multilingual bottleneck features for subword modeling in zero-resource languages", In Proceedings of Interspeech, pp. 2668–2672. 2018.

[2] E. Hermann, H. Kamper, S. Goldwater, "Multilingual and Unsupervised Subword Modeling for Zero-Resource Languages", Computer Speech and Language, Vol. 65. 2021.

@inproceedings{Hermann2018,
  title = {Multilingual {{Bottleneck Features}} for {{Subword Modeling}} in {{Zero}}-Resource {{Languages}}},
  booktitle = {Proc. {{Interspeech}}},
  author = {Hermann, Enno and Goldwater, Sharon},
  year = {2018},
  pages = {2668--2672},
  publisher = {{ISCA}},
  doi = {10.21437/Interspeech.2018-2334}
}

@article{Hermann2021,
  title = {Multilingual and {{Unsupervised Subword Modeling}} for {{Zero}}-{{Resource Languages}}},
  author = {Hermann, Enno and Kamper, Herman and Goldwater, Sharon},
  year = {2021},
  volume = {65},
  journal = {Computer Speech \& Language}
}