Employing AI for Better Access to Justice: An Automatic Text-to-Video Linking Tool for UK Supreme Court Hearings

Saadany, Hadeel and Orăsan, Constantin and Breslin, Catherine and Barczentewicz, Mikolaj and Walker, Sophie (2025) Employing AI for Better Access to Justice: An Automatic Text-to-Video Linking Tool for UK Supreme Court Hearings. Applied Sciences, 15 (16). p. 9205. ISSN 2076-3417

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Abstract

The increasing adoption of artificial intelligence across domains presents new opportunities to enhance access to justice. In this paper, we introduce a human-centric AI tool that utilises advances in Automatic Speech Recognition (ASR) and Large Language Models (LLMs) to facilitate semantic linking between written UK Supreme Court (SC) judgements and their corresponding hearing videos. The motivation stems from the critical role UK SC hearings play in shaping landmark legal decisions, which often span several hours and remain difficult to navigate manually. Our approach involves two key components: (1) a customised ASR system fine-tuned on 139 h of manually edited SC hearing transcripts and legal documents and (2) a semantic linking module powered by GPT-based text embeddings adapted to the legal domain. The ASR system addresses domain-specific transcription challenges by incorporating a custom language model and legal phrase extraction techniques. The semantic linking module uses fine-tuned embeddings to match judgement paragraphs with relevant spans in the hearing transcripts. Quantitative evaluation shows that our customised ASR system improves transcription accuracy by 9% compared to generic ASR baselines. Furthermore, our adapted GPT embeddings achieve an F1 score of 0.85 in classifying relevant links between judgement text and hearing transcript segments. These results demonstrate the effectiveness of our system in streamlining access to critical legal information and supporting legal professionals in interpreting complex judicial decisions.

Item Type:	Article
Identification Number:	10.3390/app15169205
Dates:	Date Event 7 August 2025 Accepted 20 August 2025 Published Online
Uncontrolled Keywords:	automatic speech recognition, legal transcription, UK Supreme Court, legal information retrieval, embedding customisation
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Architecture, Built Environment, Computing and Engineering > Computer Science
Depositing User:	Gemma Tonks
Date Deposited:	01 May 2026 14:07
Last Modified:	01 May 2026 14:07
URI:	https://www.open-access.bcu.ac.uk/id/eprint/17024

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