Streaming Audio-Visual Speech Recognition with Alignment Regularization

Recognizing a word shortly after it is spoken is an important requirement for automatic speech recognition (ASR) systems in real-world scenarios. As a result, a large body of work on streaming audio-only ASR models has been presented in the literature. However, streaming audio-visual automatic speech recognition (AV-ASR) has received little attention in earlier works. 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. For frame-level ASR criteria, such as CTC, a synchronized response from the audio and visual encoders is critical for a joint AV decision making process. In this work, 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.