ASR Bundestag: A Large-Scale political debate dataset in German
This dataset addresses the need for German speech data in ASR research, though it is incremental as it applies existing methods to new data.
The authors introduced ASR Bundestag, a large-scale German speech dataset with 610 hours of labeled and 1,038 hours of unlabeled audio from parliamentary sessions, and evaluated its quality by fine-tuning a state-of-the-art model.
We present ASR Bundestag, a dataset for automatic speech recognition in German, consisting of 610 hours of aligned audio-transcript pairs for supervised training as well as 1,038 hours 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.