Recently, a number of approaches to train speech models by incorpo-rating
text into end-to-end models have been developed, with Mae-stro advancing
state-of-the-art automatic speech recognition (ASR)and Speech Translation (ST)
performance. In this paper, we expandour understanding of the resulting shared
speech-text representationswith two types of analyses. First we examine the
limits of speech-free domain adaptation, finding that a corpus-specific
duration modelfor speech-text alignment is the most important component for
learn-ing a shared speech-text representation. Second, we inspect the
sim-ilarities between activations of unimodal (speech or text) encodersas
compared to the activations of a shared encoder. We find that theshared encoder
learns a more compact and overlapping speech-textrepresentation than the
uni-modal encoders. We hypothesize that thispartially explains the
effectiveness of the Maestro shared speech-textrepresentations.

本文研究基于端到端模型训练语音模型的近期方法进展，通过两种分析方法进一步扩展了对共享语音文本表示的理解：第一是研究了自由领域适应的极限，在此基础上发现针对语音文本对齐的时长模型是最重要的，这有助于学习共享语音文本表示；第二是比较了单一（语音或文本）编码器和共享编码器的激活相似性，发现共享编码器能够学习到更紧凑重叠的语音文本表示，这也是 Maestro 共享语音文本表示性能优异的部分原因。