Audiovisual materials play a fundamental role as historical and scientific records. AV materials provide evidence for every activity on earth from endangered languages to rare bird calls to the sonification of underwater, melting polar ice caps. The number of these documentary records are increasing exponentially in every field in the humanities and the sciences, and yet the professionals tasked with preserving and helping make these materials useful to scholars and the general public often lack the knowledge and resources to do so. What is a good system for those who have the responsibility for managing and preserving these assets? Generating metadata — which is essential for indexing and searchability — requires too much time if done manually. Using machine learning to generate metadata is promising, but information professionals must still overcome a host of technological and social challenges. This project addresses these challenges in a specific use case area by developing methodology and workflows for libraries, archives, and museums (LAMs) to use machine learning and supercomputing resources to generate metadata for AV materials in the humanities. We will develop and test this methodology through a pilot project that involves UTs special AV collections, the professionals that process them, and a tool being built on Texas Advanced Computing Center’s (TACC) computing resources that leverages open source speech-to-text and other machine learning (ML) applications. In the process, we will address research questions around defining and evaluating a “good” system for introducing AI for AV to information professionals.
Tanya Clement (English), Aaron Choate (Libraries), Weijia Xu (TACC)