  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.
