  Dense video captioning is a task of localizing interesting events from an
untrimmed video and producing textual description (captions) for each localized
event. Most of the previous works in dense video captioning are solely based on
visual information and completely ignore the audio track. However, audio, and
speech, in particular, are vital cues for a human observer in understanding an
environment. In this paper, we present a new dense video captioning approach
that is able to utilize any number of modalities for event description.
Specifically, we show how audio and speech modalities may improve a dense video
captioning model. We apply automatic speech recognition (ASR) system to obtain
a temporally aligned textual description of the speech (similar to subtitles)
and treat it as a separate input alongside video frames and the corresponding
audio track. We formulate the captioning task as a machine translation problem
and utilize recently proposed Transformer architecture to convert multi-modal
input data into textual descriptions. We demonstrate the performance of our
model on ActivityNet Captions dataset. The ablation studies indicate a
considerable contribution from audio and speech components suggesting that
these modalities contain substantial complementary information to video frames.
Furthermore, we provide an in-depth analysis of the ActivityNet Caption results
by leveraging the category tags obtained from original YouTube videos. Code is
publicly available: github.com/v-iashin/MDVC
