Key Points:
– Big models trained on massive unsupervised corpora in a single modality have shown remarkable results in audio and text domains.
– NYU and Google researchers have developed joint speech-text encoders to overcome sequence-length mismatch in cross-modal representations.
– The encoders use self-supervised learning to align speech and text representations.
– Experimental results demonstrate the effectiveness of the encoders in various speech and text tasks.
Author’s Take:
In a collaborative effort between NYU and Google, researchers have come up with joint speech-text encoders that tackle the challenge of sequence-length mismatch in cross-modal representations. These encoders, trained on massive unsupervised corpora, prove their mettle in handling speech and text tasks. It’s an exciting development in exploring the potential of big models and pushing the boundaries of artificial intelligence.
Original article: https://www.marktechpost.com/2023/08/17/this-paper-from-nyu-and-google-explains-how-joint-speech-text-encoders-overcome-sequence-length-mismatch-in-cross-modal-representations/