Freestyle: a challenge-response system for hip hop lyrics via unsupervised induction of stochastic transduction grammars
Dekai Wu, Karteek Addanki, Markus Saers · 2013
We attack an inexplicably under-explored language genre of spoken language-lyrics in music-via completely unsupervised induction of an SMT-style stochastic transduction grammar for hip hop lyrics, yielding a fully-automatically learned challenge-response system that produces rhyming lyrics given an input. Unlike previous efforts, we choose the domain of hip hop lyrics, which is particularly unstructured and noisy. A novel feature of our approach is that it is completely unsupervised and requires no a priori linguistic or phonetic knowledge. In spite of the level of difficulty of the challenge, the model nevertheless produces fluent output as judged by human evaluators, and performs significantly better than widely used phrase-based SMT models upon the same task.