We present a novel automatic system for performing explicit content detection directly
on the audio signal.
Our modular approach uses an audio-to-character recognition model, a keyword spotting
model associated with a dictionary of carefully chosen keywords, and a Random Forest
classification model for the final decision. To the best of our knowledge, this is the
first explicit content detection system based on audio only.
We demonstrate the individual relevance of our modules on a set of sub-tasks and compare our
approach to a lyrics-informed oracle and an end-to-end naive architecture. The results obtained
are encouraging with a F1-score of 67% on a industrial scale explicit content dataset.
This paper has been published in the proceedings of the 45th IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2020).
