  Existing speech to speech translation systems heavily rely on the text of
target language: they usually translate source language either to target text
and then synthesize target speech from text, or directly to target speech with
target text for auxiliary training. However, those methods cannot be applied to
unwritten target languages, which have no written text or phoneme available. In
this paper, we develop a translation system for unwritten languages, named as
UWSpeech, which converts target unwritten speech into discrete tokens with a
converter, and then translates source-language speech into target discrete
tokens with a translator, and finally synthesizes target speech from target
discrete tokens with an inverter. We propose a method called XL-VAE, which
enhances vector quantized variational autoencoder (VQ-VAE) with cross-lingual
(XL) speech recognition, to train the converter and inverter of UWSpeech
jointly. Experiments on Fisher Spanish-English conversation translation dataset
show that UWSpeech outperforms direct translation and VQ-VAE baseline by about
16 and 10 BLEU points respectively, which demonstrate the advantages and
potentials of UWSpeech.
