  In this work, we propose a streaming AV-ASR system based on a hybrid
connectionist temporal classification (CTC)/attention neural network
architecture. The audio and the visual encoder neural networks are both based
on the conformer architecture, which is made streamable using chunk-wise
self-attention (CSA) and causal convolution. Streaming recognition with a
decoder neural network is realized by using the triggered attention technique,
which performs time-synchronous decoding with joint CTC/attention scoring.
Additionally, we propose a novel alignment regularization technique that
promotes synchronization of the audio and visual encoder, which in turn results
in better word error rates (WERs) at all SNR levels for streaming and offline
AV-ASR models. The proposed AV-ASR model achieves WERs of 2.0% and 2.6% on the
Lip Reading Sentences 3 (LRS3) dataset in an offline and online setup,
respectively, which both present state-of-the-art results when no external
training data are used.
