The last year has seen astonishing progress in text-prompted image generation
premised on the idea of a cross-modal representation space in which the text
and image domains are represented jointly. In ASR, this idea has found
application as joint speech-text encoders that can scale to the capacities of
very large parameter models by being trained on both unpaired speech and text.
While these methods show promise, they have required special treatment of the
sequence-length mismatch inherent in speech and text, either by up-sampling
heuristics or an explicit alignment model. In this work, we offer evidence that
joint speech-text encoders naturally achieve consistent representations across
modalities by disregarding sequence length, and argue that consistency losses
could forgive length differences and simply assume the best alignment. We show
that such a loss improves downstream WER in both a large-parameter monolingual
and multilingual system.

通过跨模态表示空间的概念，在文本提示的图像生成中取得了惊人的进展。本文提出，联合语音文本编码器通过忽略序列长度差异，能够实现一致的跨模态表示，并且通过一致性损失可以改善大参数的单语言和多语言系统中的词错误率。