Unsupervised End-to-End Learning of Discrete Linguistic Units for Voice Conversion
Unsupervised End-to-End Learning of Discrete Linguistic Units for Voice Conversion
We present an unsupervised end-to-end training scheme where we discover discrete subword units from speech without using any labels. The discrete subword units are learned under an ASR-TTS autoencoder reconstruction setting, where an ASR-Encoder is trained to discover a set of common linguistic units given a variety of speakers, and …