This is a preliminary version of our system. This is not a final recipe, and is still being worked on.
A description of the challenge can be found here: http://sapience.dec.ens.fr/bootphon/2017/index.html.
The code provided here is not pretty. But I believe that research should be reproducible, and I hope that this repository is sufficient to make this possible for the paper mentioned above. I provide no guarantees with the code, but please let me know if you have any problems, find bugs or have general comments.
Clone the zerospeech repositories:
mkdir ../src/
git clone https://github.com/bootphon/zerospeech2017.git \
../src/zerospeech2017/
# To-do: add installation and data download instructions
git clone https://github.com/bootphon/zerospeech2017_surprise.git \
../src/zerospeech2017_surprise/
Clone the eskmeans repository:
git clone https://github.com/kamperh/eskmeans.git \
../src/eskmeans/
Get the surprise data:
cd ../src/zerospeech2017_surprise/
source download_surprise_data.sh \
/share/data/lang/users/kamperh/zerospeech2017/data/surprise/
cd -
Update all the paths in paths.py to match your directory structure.
Extract MFCC features by running the steps in features/readme.md.
We use the unsupervised syllable boundary detection algorithm described in:
- O. J. Räsänen, G. Doyle, and M. C. Frank, "Unsupervised word discovery from speech using automatic segmentation into syllable-like units," in Proc. Interspeech, 2015.
Obtain the syllabe boundaries by running the steps in syllables/readme.md.
We use one of the simplest methods to obtain acoustic word embeddings: downsampling. Different types of input features can be used. Run the steps in downsample/readme.md.
Segmentation and clustering is performed using the ESKMeans package. Run the steps in segmentation/readme.md.