Improving Joint Speech-Text Representations Without Alignment
Cal Peyser Zhong Meng Ke Hu Rohit Prabhavalkar Andrew Rosenberg Tara N. Sainath Michael Picheny Kyunghyun Cho
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.