End-to-end audio-visual speech recognition for overlapping speech

This paper investigates an end-to-end modeling approach for ASR that explicitly deals with scenarios where there are overlapping speech utterances from multiple talkers. The approach assumes the availability of both audio signals and video signals in the form of continuous mouth-tracks aligned with speech for overlapping speakers.
This work extends previous work on audio-only multi-talker ASR applied to two party conversations in a call center application. It also extends work on end-to-end audio-visual (A/V) ASR applied to A/V YouTube (YT) Confidence Island utterances. It is shown that incorporating attention weighted combination of visual features in A/V multi-talker RNNT models significantly improves speaker disambiguation in ASR on overlapping speech. A 17% reduction in WER was observed for A/V multi-talker models relative to audio-only multi-talker models on a simulated A/V overlapped speech corpus.