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ASR Bundestag: A Large-Scale Political Debate Dataset in German

  • Johannes Wirth,
  • René Peinl

摘要

We present ASR Bundestag, a dataset for automatic speech recognition in German, consisting of 610 h of aligned audio-transcript pairs for supervised training as well as 1,038 h of unlabeled audio snippets for self-supervised learning, based on raw audio data and transcriptions from plenary sessions and committee meetings of the German parliament. In addition, we discuss utilized approaches for the automated creation of speech datasets and assess the quality of the resulting dataset based on evaluations and finetuning of a pre-trained state of the art model. We make the dataset publicly available, including all subsets.