End-To-End Multi-Speaker Audio-Visual Automatic Speech Recognition

A singe audio-visual automated speech recognition model for transcribing speech from audio-visual data includes an encoder frontend and a decoder. The encoder includes an attention mechanism configured to receive an audio track of the audio-visual data and a video portion of the audio-visual data. The video portion of the audio-visual data includes a plurality of video face tracks each associated with a face of a respective person. For each video face track of the plurality of video face tracks, the attention mechanism is configured to determine a confidence score indicating a likelihood that the face of the respective person associated with the video face tack includes a speaking face of the audio track. The decoder is configured to process the audio track and the video face track of the plurality of video face tracks associated with the highest confidence score to determine a speech recognition result of the audio track.