Since its origins, Oral History has provided key insights into the human dimension of the past, expanding the variety of individual experiences that can inform the interpretative work of historians and museum curators. And yet, despite recent efforts to digitalize audio recordings, most of the oral history repositories are still not easily accessible, and the discovery, navigation and sense-making of this material is still a challenge. Consequently, the potential contribution of these repositories of human knowledge to enrich historical and museum practice, in a world of increasingly interoperable data, remains limited.
This investigation explored how AI, in close dialogue with the critical expertise of historians and museum curators, might offer the key to unlocking the knowledge enclosed in oral history archives and enable their connection with heritage collections and other historical sources. The overall aim of the investigation was to explore how recent advancements in Large Language Models (LLMs), speech-to-text and visual analytics can automate parts of the curatorial process, provide an innovative way to access and explore the complexities of recorded life stories, and connect them with museum objects.
This experimental work was developed within three major iterations between July 2022 and December 2024, bringing together an interdisciplinary group of digital heritage researchers, oral historians, museum curators, computer scientists and interaction design specialists. The insights as well as the tools and interfaces developed in the project have shown how a responsible use of Artificial Intelligence can introduce new possibilities not only for the use of oral histories in museums and historical research, but also for envisioning innovative, diverse and creative ways to engage with recorded life stories.
Can the advancements in Natural Language Processing help to unlock the knowledge enclosed in Oral History archives and connect them with other sources?
• How useful are oral histories in linking heritage collections?
• Can we meaningfully deploy Large Language Models (LLMs) with oral history archives? • Are new Speech-to-Text tools good enough for automated transcriptions? • Can visual analytics enable a different perspective on archives and their curatorial processes?
Stefania Zardini Lacedelli: Conceptualization, Methodology, Investigation, Data Curation, Project administration, Visualization, Writing
Tim Smith: Investigation, Data Curation, Visualization, Resources
Simon Popple: Investigation, Data Curation, Visualization
Paul Craddock; Investigation, Data Curation, Visualization
Maggie Smith: Resources
Elizabeth Llabres: Resources
John Ashton: Resources
Stef De Sabbata: Conceptualization, Methodology, Investigation, Data Curation, Formal analysis, Visualization, Software, Writing
Stefania Zardini Lacedelli: Conceptualization, Methodology, Investigation, Data Curation, Project administration, Visualization, Writing
Alex Butterworth: Methodology, Investigation, Data Curation, Writing
Andrew Richardson: Methodology, Investigation
Colin Hyde: Resources, Data Curation
Sally Horrocks: Data Curation, Validation
Daniel Belteki: Investigation, Data Curation
Neslihan Suzen: Software
Felix Needham-Simpson: Data Curation
Alison Clague: Resources
Julia Ankenbrand: Resources
A group of stories of living and working in Saltaire, collected, recorded and transcribed by Collection volunteers. For the purpose of the investigation, one transcribed life story was used.
A major project set up by Bradford Museums, Galleries and Libraries from 1983 to 2006 with the aim of capturing the memories, reflections, contemporary attitudes and images of Bradford people of all ages, classes and races. For the purpose of this investigation, 800 audio interviews (available as digital audio files) were collected, including a consistent corpus of life stories of individuals of European, Asian and Afro Caribbean origins who came to work in the textile industry.
A corpus of 23 mining interviews with mining workers at Snibston and Whitwick collieries, aimed at enhancing the interpretation of the mining collections of the Snibston Museum. Once the museum closed in 2016, the interviews - comprising both digital audio files and digitized transcripts – were made available as part of the East Midlands Oral History Archive. As part of the investigation, the museum collection data was also collected from Leicestershire County Council.
Named Entity Recognition, Fuzzy Matching, Manual Annotation, Digital Curatorial Platforms (Omeka and Yarn)
Speech to Text (OpenAI Whisper), Topic Modelling (LLM-based), Visual Analytics (Plotly and Dash python libraries)
A full description of each stage of the pipeline (and related code developed) is available in the repository.
Two digital narratives were created on two digital platforms – Omeka and Yarn – with the aim to visualize the connections with museum objects, historical sources and online repositories which are enclosed in a life story.
A proof-of-concept interactive dashboard which allows users to explore a web of automatic-generated topics from the Mines of Memory and visualize the connections of specific sections of the interviews with museum objects from the Snibston Museum. In a first visualization, the interviews are represented as bars, each splitted into one section per paragraph. Clicking on a bar section activates a side section where the transcription and a suggested matched object from the museum collection is shown. In a second visualization, the sentences are represented as dots, which are arranged on the screen based on their similarity, thus allowing the user to manually identify interesting groupings.
This work is licensed under a Creative Commons Attribution 4.0 License - CC BY 4.0.