TranscribeSight : redefining ASR evaluation for Industry 4.0 knowledge capture

This paper presents TranscribeSight, a benchmarking platform for systematic evaluation of automatic speech recognition (ASR) technologies in industrial knowledge management contexts. As Industry 4.0 transforms manufacturing, organisations require flexible frameworks for comparing traditional and multimodal large language model solutions. TranscribeSight provides an extensible platform integrating multidimensional evaluation methodologies, encompassing semantic preservation metrics, operational efficiency analysis, and automated qualitative assessment capabilities. The platform’s modular architecture enables integration of diverse ASR services and customisation of evaluation criteria for specific industrial requirements. We demonstrate capabilities through controlled evaluation of eight ASR services using LibriSpeech audio samples with English-language content and default API configurations, though the platform supports multilingual extension and customised configurations. The evaluation identified performance variations across services, with different technologies excelling in accuracy, semantic preservation, or operational efficiency. TranscribeSight provides a foundation for systematic, reproducible ASR assessment adapting to technological advancement and diverse operational requirements in Industry 4.0 environments.

Publication Details

Journal
Strathprints: The University of Strathclyde institutional repository (University of Strathclyde)
Published
2026-03-23
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

TranscribeSight : redefining ASR evaluation for Industry 4.0 knowledge capture

Strathprints: The University of Strathclyde institutional repository (University of Strathclyde)
Geochemistry and Geologic Mapping
article

TranscribeSight : redefining ASR evaluation for Industry 4.0 knowledge capture

article en

Abstract

This paper presents TranscribeSight, a benchmarking platform for systematic evaluation of automatic speech recognition (ASR) technologies in industrial knowledge management contexts. As Industry 4.0 transforms manufacturing, organisations require flexible frameworks for comparing traditional and multimodal large language model solutions. TranscribeSight provides an extensible platform integrating multidimensional evaluation methodologies, encompassing semantic preservation metrics, operational efficiency analysis, and automated qualitative assessment capabilities. The platform’s modular architecture enables integration of diverse ASR services and customisation of evaluation criteria for specific industrial requirements. We demonstrate capabilities through controlled evaluation of eight ASR services using LibriSpeech audio samples with English-language content and default API configurations, though the platform supports multilingual extension and customised configurations. The evaluation identified performance variations across services, with different technologies excelling in accuracy, semantic preservation, or operational efficiency. TranscribeSight provides a foundation for systematic, reproducible ASR assessment adapting to technological advancement and diverse operational requirements in Industry 4.0 environments.

Strathprints: The University of Strathclyde institutional repository (University of Strathclyde)
Industry, innovation and infrastructure
Openalex Percentile: Top 98%
Geochemistry and Geologic Mapping
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.