  This paper presents an audio visual automatic speech recognition (AV-ASR)
system using a Transformer-based architecture. We particularly focus on the
scene context provided by the visual information, to ground the ASR. We extract
representations for audio features in the encoder layers of the transformer and
fuse video features using an additional crossmodal multihead attention layer.
Additionally, we incorporate a multitask training criterion for multiresolution
ASR, where we train the model to generate both character and subword level
transcriptions.
  Experimental results on the How2 dataset, indicate that multiresolution
training can speed up convergence by around 50% and relatively improves word
error rate (WER) performance by upto 18% over subword prediction models.
Further, incorporating visual information improves performance with relative
gains upto 3.76% over audio only models.
  Our results are comparable to state-of-the-art Listen, Attend and Spell-based
architectures.
