ASR Bundestag: A Large-Scale political debate dataset in German

TL;DR

The paper introduces ASR Bundestag, a comprehensive German speech dataset with extensive labeled and unlabeled audio for advancing speech recognition, along with evaluation and dataset creation methods.

Contribution

It provides a large-scale, publicly available German political debate dataset and discusses automated dataset creation and quality assessment methods.

Findings

Dataset enables improved German speech recognition models

Automated dataset creation approaches are effective

Pre-trained models benefit from the dataset for fine-tuning

Abstract

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.