# Better Transcription of UK Supreme Court Hearings

arXiv: https://arxiv.org/abs/2211.17094 · HTML: https://mlsanity.com/paper/2211.17094/

## 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 (most cited first, 9 of 9)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings](https://mlsanity.com/paper/2004.09249/) (2020)
- [Earnings-21: A Practical Benchmark for ASR in the Wild](https://mlsanity.com/paper/2104.11348/) (2021)
- [Quantifying Bias in Automatic Speech Recognition](https://mlsanity.com/paper/2103.15122/) (2021)
- [ASR Error Correction and Domain Adaptation Using Machine Translation](https://mlsanity.com/paper/2003.07692/) (2020)
- [Efficient minimum word error rate training of RNN-Transducer for end-to-end speech recognition](https://mlsanity.com/paper/2007.13802/) (2020)
- [BERT Goes to Law School: Quantifying the Competitive Advantage of Access to Large Legal Corpora in Contract Understanding](https://mlsanity.com/paper/1911.00473/) (2019)
- [Unsupervised domain adaptation for speech recognition with unsupervised error correction](https://mlsanity.com/paper/2209.12043/) (2022)
- [Incremental Layer-wise Self-Supervised Learning for Efficient Speech Domain Adaptation On Device](https://mlsanity.com/paper/2110.00155/) (2021)

## Built on by (most cited first, 3 of 3)

- [Lost in Transcription, Found in Distribution Shift: Demystifying Hallucination in Speech Foundation Models](https://mlsanity.com/paper/2502.12414/) (2025)
- [A Deep Dive into the Disparity of Word Error Rates Across Thousands of NPTEL MOOC Videos](https://mlsanity.com/paper/2307.10587/) (2023)
- [AfriVox-v2: A Domain-Verticalized Benchmark for In-the-Wild African Speech Recognition](https://mlsanity.com/paper/2605.03590/) (2026)

## 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.