  The research in machine translation community focus on translation in text
space. However, humans are in fact also good at direct translation in
pronunciation space. Some existing translation systems, such as simultaneous
machine translation, are inherently more natural and thus potentially more
robust by directly translating in pronunciation space. In this paper, we
conduct large scale experiments on a self-built dataset with about $20$M En-Zh
pairs of text sentences and corresponding pronunciation sentences. We proposed
three new categories of translations: $1)$ translating a pronunciation sentence
in source language into a pronunciation sentence in target language (P2P-Tran),
$2)$ translating a text sentence in source language into a pronunciation
sentence in target language (T2P-Tran), and $3)$ translating a pronunciation
sentence in source language into a text sentence in target language (P2T-Tran),
and compare them with traditional text translation (T2T-Tran). Our experiments
clearly show that all $4$ categories of translations have comparable
performances, with small and sometimes ignorable differences.

Computation and Language