Unpaired Conversational Dataset: The proprietary dataset described in Jia et al. [2019a] was
obtained by crowd-sourcing humans to read the both sides of a conversational Spanish-English MT
dataset. We synthesized both source and target speech using a multi-lingual multi-speaker Phoneme-
and-Grapheme Non-Attentive Tacotron (PnG NAT) TTS model Jia et al. [2021], Shen et al. [2020]
and a WaveRNN-based neural vocoder Kalchbrenner et al. [2018]. These TTS and vocoder models
were trained from proprietary TTS datasets with more than 1,000 speakers. To prevent that data from
being paired, we split the data in half and for one half pick English as the source language and for the
other half pick Spanish as the source language. For the training set, we ended up with ∼379K English
utterances that were ∼371 hours of speech and ∼379K Spanish utterances that were ∼350 hours
of speech. For the test set we used ∼6.5K paired utterances that were ∼5.4 hours in English and
Translatotron3