  This paper presents Translatotron 3, a novel approach to unsupervised direct
speech-to-speech translation from monolingual speech-text datasets by combining
masked autoencoder, unsupervised embedding mapping, and back-translation.
Experimental results in speech-to-speech translation tasks between Spanish and
English show that Translatotron 3 outperforms a baseline cascade system,
reporting $18.14$ BLEU points improvement on the synthesized
Unpaired-Conversational dataset. In contrast to supervised approaches that
necessitate real paired data, or specialized modeling to replicate
para-/non-linguistic information such as pauses, speaking rates, and speaker
identity, Translatotron 3 showcases its capability to retain it. Audio samples
can be found at http://google-research.github.io/lingvo-lab/translatotron3
