Multimodal Digital Oral History

About the Project

The Multimodal Digital Oral History project is developing a methodology and technical workflow for active engagement with the oral, aural and sonic affordances of both retro-digitised and born-digital oral history collections, across modalities including transcript, sound, waveform, speсtrogram and metadata .

At present, we are working on the detection of laughter in oral history interviews using AI tools, in combination with non-digital approaches. Laughter provides a compelling example of elementssuch as hesitations, vocal quirks, and pacingtypically omitted from manual transcripts.

Methodology

Our approach encompasses multiple modalities of engagement with oral history materials, moving beyond traditional text-based analysis to incorporate the full spectrum of sonic and multimodal information contained within digital oral history collections.

We work across transcript, sound, waveform and metadata to develop comprehensive methodologies that preserve and analyze the rich, multidimensional nature of oral historical records. This multimodal approach enables new forms of scholarship that honor both the content and the form of oral historical testimony.

Technical Innovation

AI routines enable the study of vocal features at the level of individual interviews and across larger collections. By combining computational approaches with traditional oral history methodologies, we can identify and analyze patterns that would be impossible to detect through manual transcription alone.

This work represents a significant advancement in digital humanities methodology, opening new possibilities for understanding the full communicative richness of oral historical materials.

The transcript visualizer & editor is under active development.