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.

翻译：音视频语音识别因其对噪声的鲁棒性而备受关注。近年来，自动语音识别、视觉语音识别和音视频语音识别（分别简称为ASR、VSR和AV-ASR）的性能得到了显著提升，这主要归功于更大规模模型和训练数据集的使用。然而，数据集的精确标注既耗时又昂贵。因此，本工作探究了利用未标注数据集的自动生成转录文本来扩充训练集规模的方法。为此，我们借助公开可用的预训练ASR模型，自动转录了AVSpeech和VoxCeleb2等未标注数据集。随后，在由LRS2和LRS3数据集以及额外自动转录数据构成的增强训练集上，训练了ASR、VSR和AV-ASR模型。我们证明，尽管使用了含噪声的转录文本，但扩大训练集规模这一文献中的最新趋势仍能降低词错误率（WER）。所提出的模型在LRS2和LRS3数据集上的AV-ASR任务中取得了新的最佳性能。具体而言，该模型在LRS3数据集上的WER达到0.9%，较当前最优方法相对提升了30%，并且优于那些在非公开可用数据集（训练数据量为其26倍）上训练的方法。