  Transcription or sub-titling of open-domain videos is still a challenging
domain for Automatic Speech Recognition (ASR) due to the data's challenging
acoustics, variable signal processing and the essentially unrestricted domain
of the data. In previous work, we have shown that the visual channel --
specifically object and scene features -- can help to adapt the acoustic model
(AM) and language model (LM) of a recognizer, and we are now expanding this
work to end-to-end approaches. In the case of a Connectionist Temporal
Classification (CTC)-based approach, we retain the separation of AM and LM,
while for a sequence-to-sequence (S2S) approach, both information sources are
adapted together, in a single model. This paper also analyzes the behavior of
CTC and S2S models on noisy video data (How-To corpus), and compares it to
results on the clean Wall Street Journal (WSJ) corpus, providing insight into
the robustness of both approaches.
