Better Transcription of UK Supreme Court Hearings
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Abstract (arXiv)
Transcription of legal proceedings is very important to enable access to justice. However, speech transcription is an expensive and slow process. In this paper we describe part of a combined research and industrial project for building an automated transcription tool designed specifically for the Justice sector in the UK. We explain the challenges involved in transcribing court room hearings and the Natural Language Processing (NLP) techniques we employ to tackle these challenges. We will show that fine-tuning a generic off-the-shelf pre-trained Automatic Speech Recognition (ASR) system with an in-domain language model as well as infusing common phrases extracted with a collocation detection model can improve not only the Word Error Rate (WER) of the transcribed hearings but avoid critical errors that are specific of the legal jargon and terminology commonly used in British courts.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 9 of its 22 references that are in the map. First posted on arXiv 29 Nov 2022.
Builds on
9 of 22 references.
References · 9
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
- 2020CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordingscited by 132
- 2021Earnings-21: A Practical Benchmark for ASR in the Wildcited by 62
- 2021Quantifying Bias in Automatic Speech Recognitioncited by 50
- 2020ASR Error Correction and Domain Adaptation Using Machine Translationcited by 48
- 2020Efficient minimum word error rate training of RNN-Transducer for end-to-end speech recognitioncited by 34
- 2019BERT Goes to Law School: Quantifying the Competitive Advantage of Access to Large Legal Corpora in Contract Understandingcited by 16
- 2022Unsupervised domain adaptation for speech recognition with unsupervised error correctioncited by 5
- 2021Incremental Layer-wise Self-Supervised Learning for Efficient Speech Domain Adaptation On Devicecited by 3
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 1.
3 papers.
- 2025Lost in Transcription, Found in Distribution Shift: Demystifying Hallucination in Speech Foundation Modelscited by 18
- 2023A Deep Dive into the Disparity of Word Error Rates Across Thousands of NPTEL MOOC Videoscited by 5
- 2026AfriVox-v2: A Domain-Verticalized Benchmark for In-the-Wild African Speech Recognitioncited by 0
- 2026AfriVox-v2: A Domain-Verticalized Benchmark for In-the-Wild African Speech Recognition5 May 2026
- 2025Lost in Transcription, Found in Distribution Shift: Demystifying Hallucination in Speech Foundation Models18 Feb 2025
- 2023A Deep Dive into the Disparity of Word Error Rates Across Thousands of NPTEL MOOC Videos20 Jul 2023