Merkel Podcast Corpus: A Multimodal Dataset Compiled from 16 Years of Angela Merkel's Weekly Video Podcasts
This dataset provides a valuable resource for researchers working on multimodal AI, particularly in German language processing, though it is incremental as it applies existing methods to new data.
The authors introduced the Merkel Podcast Corpus, a multimodal dataset in German compiled from 16 years of Angela Merkel's weekly video podcasts, addressing the lack of a comparable single-speaker corpus with audio, visual, and text modalities, and demonstrated its utility through statistical analyses and applications in talking face generation and TTS.
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.