asalekin-ubiquitouslab/dare-gp

Develop universal adversarial perturbations that protect user emotion in speech without disrupting transcription

DARE-GP

Develop universal adversarial perturbations that protect user emotion in speech without disrupting transcription.

This code uses the RAVDESS (https://www.kaggle.com/datasets/uwrfkaggler/ravdess-emotional-speech-audio) and TESS (https://www.kaggle.com/datasets/ejlok1/toronto-emotional-speech-set-tess) datasets. Use the fix_ravdess.py and fix_tess.py scripts to rename these to the correct naming convention.

This code requires very specific library versions (specifically for the vosk/kaldi transcription code). See the docker/Dockerfile file for specifics.

To cite this work, please include this reference:

@article{testa2023privacy, title={Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learning}, author={Testa, Brian and Xiao, Yi and Sharma, Harshit and Gump, Avery and Salekin, Asif}, journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies}, volume={7}, number={3}, pages={1--30}, year={2023}, publisher={ACM New York, NY, USA} }