End-to-end multi-talker audio-visual ASR using an active speaker attention module
This addresses the label ambiguity issue in multi-talker ASR for applications like video transcription, though it is incremental as it builds on existing transformer-transducer methods.
The paper tackles the problem of assigning decoded text to specific speakers in multi-talker audio-visual speech recognition by introducing a visual context attention model (VCAM), which improves performance over prior audio-only and audio-visual systems on a two-speaker overlapping speech dataset.
This paper presents a new approach for end-to-end audio-visual multi-talker speech recognition. The approach, referred to here as the visual context attention model (VCAM), is important because it uses the available video information to assign decoded text to one of multiple visible faces. This essentially resolves the label ambiguity issue associated with most multi-talker modeling approaches which can decode multiple label strings but cannot assign the label strings to the correct speakers. This is implemented as a transformer-transducer based end-to-end model and evaluated using a two speaker audio-visual overlapping speech dataset created from YouTube videos. It is shown in the paper that the VCAM model improves performance with respect to previously reported audio-only and audio-visual multi-talker ASR systems.