disvoice-phonological 0.0.1

A pip installable version of the phonological function from jcvazquezc's DisVoice library

Phonological features

Phonological features

phonological.py

Compute phonological features from continuous speech files.

18 descriptors are computed, bases on 18 different phonological classes from the phonet toolkit https://phonet.readthedocs.io/en/latest/?badge=latest

It computes the phonological log-likelihood ratio features from phonet

Static or dynamic matrices can be computed:

Static matrix is formed with 108 features formed with (18 descriptors) x (6 functionals: mean, std, skewness, kurtosis, max, min)

Dynamic matrix is formed with the 18 descriptors computed for frames of 25 ms with a time-shift of 10 ms.

Running

Script is called as follows

python phonological.py <file_or_folder_audio> <file_features> <static (true or false)> <plots (true or false)> <format (csv, txt, npy, kaldi, torch)>

Examples:

Extract features in the command line

python phonological.py "../audios/001_ddk1_PCGITA.wav" "phonologicalfeaturesAst.txt" "true" "true" "txt"
python phonological.py "../audios/001_ddk1_PCGITA.wav" "phonologicalfeaturesUst.csv" "true" "true" "csv"
python phonological.py "../audios/001_ddk1_PCGITA.wav" "phonologicalfeaturesUdyn.pt" "false" "true" "torch"

python phonological.py "../audios/" "phonologicalfeaturesst.txt" "true" "false" "txt"
python phonological.py "../audios/" "phonologicalfeaturesst.csv" "true" "false" "csv"
python phonological.py "../audios/" "phonologicalfeaturesdyn.pt" "false" "false" "torch"
python phonological.py "../audios/" "phonologicalfeaturesdyn.csv" "false" "false" "csv"

KALDI_ROOT=/home/camilo/Camilo/codes/kaldi-master2
export PATH=$PATH:$KALDI_ROOT/src/featbin/
python phonological.py "../audios/001_ddk1_PCGITA.wav" "phonologicalfeaturesddk1dyn" "false" "false" "kaldi"

python phonological.py "../audios/" "phonologicalfeaturesdyn" "false" "false" "kaldi"

Extract features directly in Python

phonological=Phonological()
file_audio="../audios/001_ddk1_PCGITA.wav"
features1=phonological.extract_features_file(file_audio, static=True, plots=True, fmt="npy")
features2=phonological.extract_features_file(file_audio, static=True, plots=True, fmt="dataframe")
features3=phonological.extract_features_file(file_audio, static=False, plots=True, fmt="torch")
phonological.extract_features_file(file_audio, static=False, plots=False, fmt="kaldi", kaldi_file="./test")

path_audio="../audios/"
features1=phonological.extract_features_path(path_audio, static=True, plots=False, fmt="npy")
features2=phonological.extract_features_path(path_audio, static=True, plots=False, fmt="csv")
features3=phonological.extract_features_path(path_audio, static=False, plots=True, fmt="torch")
phonological.extract_features_path(path_audio, static=False, plots=False, fmt="kaldi", kaldi_file="./test.ark")

Jupyter notebook

Results:

Phonological analysis ! ! !

References

[1] Vásquez-Correa, J. C., Klumpp, P., Orozco-Arroyave, J. R., & Nöth, E. (2019). Phonet: A Tool Based on Gated Recurrent Neural Networks to Extract Phonological Posteriors from Speech. In INTERSPEECH (pp. 549-553).

[2] Diez, M., Varona, A., Penagarikano, M., Rodriguez-Fuentes, L. J., & Bordel, G. (2014). On the projection of PLLRs for unbounded feature distributions in spoken language recognition. IEEE Signal Processing Letters, 21(9), 1073-1077.