113 citations · 278 across the 21 of their papers we have counts for
11 papers · 1 filter
Using Text Injection to Improve Recognition of Personal Identifiers in Speech
Yochai Blau, Rohan Agrawal, Lior Madmony +7
Accurate recognition of specific categories, such as persons' names, dates or other identifiers is critical in many Automatic Speech Recognition (ASR) applications. As these catego…
Improving Joint Speech-Text Representations Without Alignment
Cal Peyser, Zhong Meng, Ke Hu +5
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 rep…
Understanding Shared Speech-Text Representations
Gary Wang, Kyle Kastner, Ankur Bapna +4
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…
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages
Yu Zhang, Wei Han, James Qin +24
We introduce the Universal Speech Model (USM), a single large model that performs automatic speech recognition (ASR) across 100+ languages. This is achieved by pre-training the enc…
Maestro-U: Leveraging joint speech-text representation learning for zero supervised speech ASR
Zhehuai Chen, Ankur Bapna, Andrew Rosenberg +4
Training state-of-the-art Automated Speech Recognition (ASR) models typically requires a substantial amount of transcribed speech. In this work, we demonstrate that a modality-matc…
MAESTRO: Matched Speech Text Representations through Modality Matching
Zhehuai Chen, Yu Zhang, Andrew Rosenberg +4
We present Maestro, a self-supervised training method to unify representations learnt from speech and text modalities. Self-supervised learning from speech signals aims to learn th…