Voices as Data: Computational Phonetics and Responsible AI for Cultural Soundscapes
Instructors:
Katarzyna Foremniak,Mariusz Sozański
Duration: both weeks
While Digital Humanities has extensively explored textual and visual sources, the computational analysis of speech and vocal performance remains underrepresented. This two-week workshop introduces voice as structured cultural data and critically examines how AI technologies mediate access to vocal heritage. Participants will learn how to design and curate small speech corpora, extract acoustic and prosodic features, and apply basic statistical models to analyze phonetic variation. Building on these foundations, the workshop integrates AI-based speech technologies, including automatic speech recognition and large language models, with a strong emphasis on evaluation, bias detection, and epistemic risk. Rather than focusing on tool usage alone, the course foregrounds responsible AI practices, transparency, and reproducibility in the analysis of spoken cultural data. Through hands-on sessions using open datasets such as Mozilla Common Voice and selected oral history recordings, participants will collaboratively build a reusable, open-source research toolkit hosted on GitHub. This toolkit will include containerized workflows, analysis notebooks, documentation, and a responsible-AI framework tailored to speech data in humanities research. The workshop combines computational practice, infrastructural awareness, and critical reflection, empowering participants from diverse disciplinary backgrounds to treat voice as analyzable cultural evidence while remaining attentive to ethical, methodological, and social implications. No advanced programming skills are required.