In this repository, you'll find all the necessary code for applying a pre-trained model that, given an audio recording, classifies each frame into [SPEECH, KCHI, CHI, MAL, FEM].
- FEM stands for female speech
- MAL stands for male speech
- KCHI stands for key-child speech
- CHI stands for other child speech
- SPEECH stands for speech :)
Our model's architecture is based on SincNet [3] and LSTM layers. Details can be found in our paper [1]. The code mainly relies on pyannote-audio [2], an awesome python toolkit for building neural building blocks that can be combined to solve the speaker diarization task.
ALICE, an Automatic Linguistic Unit Count Estimator, allowing you to count the number of words, syllables and phonemes in adult speakers' utterances :
[1] An open-source voice type classifier for child-centered daylong recordings -under review-
@misc{lavechin2020opensource,
title={An open-source voice type classifier for child-centered daylong recordings},
author={Marvin Lavechin and Ruben Bousbib and Hervé Bredin and Emmanuel Dupoux and Alejandrina Cristia},
year={2020},
eprint={2005.12656},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
[2] pyannote.audio: neural building blocks for speaker diarization
@inproceedings{Bredin2020,
Title = {{pyannote.audio: neural building blocks for speaker diarization}},
Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe},
Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing},
Address = {Barcelona, Spain},
Month = {May},
Year = {2020},
}
[3] Speaker Recognition from Raw Waveform with SincNet
@misc{ravanelli2018speaker,
title={Speaker Recognition from Raw Waveform with SincNet},
author={Mirco Ravanelli and Yoshua Bengio},
year={2018},
eprint={1808.00158},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
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