  Audio-visual information fusion enables a performance improvement in speech
recognition performed in complex acoustic scenarios, e.g., noisy environments.
It is required to explore an effective audio-visual fusion strategy for
audiovisual alignment and modality reliability. Different from the previous
end-to-end approaches where the audio-visual fusion is performed after encoding
each modality, in this paper we propose to integrate an attentive fusion block
into the encoding process. It is shown that the proposed audio-visual fusion
method in the encoder module can enrich audio-visual representations, as the
relevance between the two modalities is leveraged. In line with the
transformer-based architecture, we implement the embedded fusion block using a
multi-head attention based audiovisual fusion with one-way or two-way
interactions. The proposed method can sufficiently combine the two streams and
weaken the over-reliance on the audio modality. Experiments on the LRS3-TED
dataset demonstrate that the proposed method can increase the recognition rate
by 0.55%, 4.51% and 4.61% on average under the clean, seen and unseen noise
conditions, respectively, compared to the state-of-the-art approach.
