arXiv:1912.06979cs.HC
Breaking Speech Recognizers to Imagine Lyrics
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Abstract
We introduce a new method for generating text, and in particular song lyrics, based on the speech-like acoustic qualities of a given audio file. We repurpose a vocal source separation algorithm and an acoustic model trained to recognize isolated speech, instead inputting instrumental music or environmental sounds. Feeding the "mistakes" of the vocal separator into the recognizer, we obtain a transcription of words \emph{imagined} to be spoken in the input audio. We describe the key components of our approach, present initial analysis, and discuss the potential of the method for machine-in-the-loop collaboration in creative applications.
> As far as I know, those are for experiments that directly interact with humans as part of the experiment itself. Or use certain kinds of data from humans. For example, a machine learning project on medical data (even anonymized medical data) probably needs an IRB approval to…
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