Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

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 …

We summarize the results of a host of efforts using giant automatic speech recognition (ASR)
models pre-trained using large, diverse unlabeled datasets containing approximately a …

In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …

Personal rare word recognition in end-to-end Automatic Speech Recognition (E2E ASR)
models is a challenge due to the lack of training data. A standard way to address this issue …

Building ASR models across many languages is a challenging multi-task learning problem
due to large variations and heavily unbalanced data. Existing work has shown positive …

We present JOIST, an algorithm to train a streaming, cascaded, encoder end-to-end (E2E)
model with both speech-text paired inputs, and text-only unpaired inputs. Unlike previous …

Previous works on the Recurrent Neural Network-Transducer (RNN-T) models have shown
that, under some conditions, it is possible to simplify its prediction network with little or no …

The development of end-to-end models has largely sped up the research in massively
multilingual automatic speech recognition (MMASR). Previous research has demonstrated …