  In this paper, we present a transcribed corpus of the LIBE committee of the
EU parliament, totalling 3.6 Million running words. The meetings of
parliamentary committees of the EU are a potentially valuable source of
information for political scientists but the data is not readily available
because only disclosed as speech recordings together with limited metadata. The
meetings are in English, partly spoken by non-native speakers, and partly
spoken by interpreters. We investigated the most appropriate Automatic Speech
Recognition (ASR) model to create an accurate text transcription of the audio
recordings of the meetings in order to make their content available for
research and analysis. We focused on the unsupervised domain adaptation of the
ASR pipeline. Building on the transformer-based Wav2vec2.0 model, we
experimented with multiple acoustic models, language models and the addition of
domain-specific terms. We found that a domain-specific acoustic model and a
domain-specific language model give substantial improvements to the ASR output,
reducing the word error rate (WER) from 28.22 to 17.95. The use of
domain-specific terms in the decoding stage did not have a positive effect on
the quality of the ASR in terms of WER. Initial topic modelling results
indicated that the corpus is useful for downstream analysis tasks. We release
the resulting corpus and our analysis pipeline for future research.
