  We introduce the Merkel Podcast Corpus, an audio-visual-text corpus in German
collected from 16 years of (almost) weekly Internet podcasts of former German
chancellor Angela Merkel. To the best of our knowledge, this is the first
single speaker corpus in the German language consisting of audio, visual and
text modalities of comparable size and temporal extent. We describe the methods
used with which we have collected and edited the data which involves
downloading the videos, transcripts and other metadata, forced alignment,
performing active speaker recognition and face detection to finally curate the
single speaker dataset consisting of utterances spoken by Angela Merkel. The
proposed pipeline is general and can be used to curate other datasets of
similar nature, such as talk show contents. Through various statistical
analyses and applications of the dataset in talking face generation and TTS, we
show the utility of the dataset. We argue that it is a valuable contribution to
the research community, in particular, due to its realistic and challenging
material at the boundary between prepared and spontaneous speech.
