Improving Joint Speech-Text Representations Without Alignment
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.
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Cal Peyser
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Zhong Meng
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Ke Hu
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Rohit Prabhavalkar
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Andrew Rosenberg
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Tara N. Sainath
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Michael Picheny
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Kyunghyun Cho
admin_assigned
2023-08-15T00:52:02.244Z
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/avatars/29080f550fdb471c4d2daace86aff3b0.svg
Kyunghyun Cho
user
kyunghyuncho
Improving Joint Speech-Text Representations Without Alignment
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.
Joint speech-text encoders achieve consistent cross-modal representations without specific sequence-length treatment, improving downstream WER through consistency losses.
cross-modal representation space joint speech-text encoders sequence-length mismatch up-sampling heuristics explicit alignment model consistency losses word error rate (WER)