ASR Bundestag: A Large-Scale political debate dataset in German
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 22 of its 43 references that are in the map. First posted on arXiv 12 Feb 2023.
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22 of 43 references.
References · 22
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10 more
- 2019pyannote.audio: neural building blocks for speaker diarizationcited by 175
- 2021SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Networkcited by 83
- 2021SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognitioncited by 77
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