  Audio-visual speech recognition has received a lot of attention due to its
robustness against acoustic noise. Recently, the performance of automatic,
visual, and audio-visual speech recognition (ASR, VSR, and AV-ASR,
respectively) has been substantially improved, mainly due to the use of larger
models and training sets. However, accurate labelling of datasets is
time-consuming and expensive. Hence, in this work, we investigate the use of
automatically-generated transcriptions of unlabelled datasets to increase the
training set size. For this purpose, we use publicly-available pre-trained ASR
models to automatically transcribe unlabelled datasets such as AVSpeech and
VoxCeleb2. Then, we train ASR, VSR and AV-ASR models on the augmented training
set, which consists of the LRS2 and LRS3 datasets as well as the additional
automatically-transcribed data. We demonstrate that increasing the size of the
training set, a recent trend in the literature, leads to reduced WER despite
using noisy transcriptions. The proposed model achieves new state-of-the-art
performance on AV-ASR on LRS2 and LRS3. In particular, it achieves a WER of
0.9% on LRS3, a relative improvement of 30% over the current state-of-the-art
approach, and outperforms methods that have been trained on non-publicly
available datasets with 26 times more training data.
