The State of Commercial Automatic French Legal Speech Recognition Systems and their Impact on Court Reporters et al

TL;DR

This study evaluates commercial and open-source French legal speech recognition systems, benchmarking their accuracy and discussing their potential impact on court reporting efficiency and the legal system.

Contribution

It provides a comparative analysis of ASR models for French legal speech and discusses the implications of adopting such technology in court transcription.

Findings

Current ASR systems show promise but need refinement for legal use

Benchmarking includes WER and phonetic accuracy metrics

Impacts on court reporters and legal proceedings are discussed

Abstract

In Quebec and Canadian courts, the transcription of court proceedings is a critical task for appeal purposes and must be certified by an official court reporter. The limited availability of qualified reporters and the high costs associated with manual transcription underscore the need for more efficient solutions. This paper examines the potential of Automatic Speech Recognition (ASR) systems to assist court reporters in transcribing legal proceedings. We benchmark three ASR models, including commercial and open-source options, on their ability to recognize French legal speech using a curated dataset. Our study evaluates the performance of these systems using the Word Error Rate (WER) metric and introduces the Sonnex Distance to account for phonetic accuracy. We also explore the broader implications of ASR adoption on court reporters, copyists, the legal system, and litigants, identifying both positive and negative impacts. The findings suggest that while current ASR systems show promise, they require further refinement to meet the specific needs of the legal domain.