This toolkit compute posteriors probabilities of phonological classes from audio files for several groups of phonemes according to the mode and manner of articulation.

If you are not sure about what phonological classes are, have a look at this Phonological classes tutorial

Project Documentation

Paper

The list of the phonological classes available and the phonemes that are activated for each phonological class are observed in the following Table

The list of the phonological classes available and the phonemes that are activated for each phonological class are observed in the following Table

From this repository:

git clone https://github.com/jcvasquezc/phonet
cd phonet
python setup.py

Supported features:

• Estimate probabilities of phonological classes for an audio file

Example use

Estimate the phonological classes using the BGRU models for an audio file or for a folder that contains audio files inside:

python
phon=Phonet([phonclass])
get_phon_wav(self, audio_file, feat_file, plot_flag=True)

If you want to train Phonet in your own language, or specific phonological classes that are not defined here, please refer to the folder train and follow the instructions there.

If you experienced problems with the Training process, please send me an email <[email protected]>

Phonet is available for research purposes

If you use Phonet, please cite the following paper.

@inproceedings{Vasquez-Correa2019,

author={J. C. Vásquez-Correa and P. Klumpp and J. R. Orozco-Arroyave and E. N"oth}, title={{Phonet: A Tool Based on Gated Recurrent Neural Networks to Extract Phonological Posteriors from Speech}}, year=2019, booktitle={Proc. Interspeech 2019}, pages={549--553}, doi={10.21437/Interspeech.2019-1405}, url={http://dx.doi.org/10.21437/Interspeech.2019-1405}

}