Understanding Shared Speech-Text Representations

• URL: http://arxiv.org/abs/2304.14514v1
• Date: Thu, 27 Apr 2023 20:05:36 GMT
• Title: Understanding Shared Speech-Text Representations
• Authors: Gary Wang, Kyle Kastner, Ankur Bapna, Zhehuai Chen, Andrew Rosenberg, Bhuvana Ramabhadran, Yu Zhang
• Abstract summary: Mae-stro has developed approaches to train speech models by incorpo-rating text into end-to-end models. We find that a corpus-specific duration modelfor speech-text alignment is the most important component for learn-ing a shared speech-text representation. We find that theshared encoder learns a more compact and overlapping speech-textrepresentation than the uni-modal encoders.
• Score: 34.45772613231558
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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