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Computer Science > Computation and Language

arXiv:2304.14514 (cs)
[Submitted on 27 Apr 2023]

Title:Understanding Shared Speech-Text Representations

Authors:Gary Wang, Kyle Kastner, Ankur Bapna, Zhehuai Chen, Andrew Rosenberg, Bhuvana Ramabhadran, Yu Zhang
View a PDF of the paper titled Understanding Shared Speech-Text Representations, by Gary Wang and 6 other authors
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Abstract: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.
Comments: Accepted at ICASSP 2023, camera ready
Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
Cite as: arXiv:2304.14514 [cs.CL]
  (or arXiv:2304.14514v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2304.14514
arXiv-issued DOI via DataCite

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From: Gary Wang [view email]
[v1] Thu, 27 Apr 2023 20:05:36 UTC (1,031 KB)
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