There is a vast treasure trove of free Latin language pedagogical content available on the Internet, but shockingly little of it is in plaintext—the greppable, diffable, interoperable, and eternal format that is the fundamental substrate of GNU Emacs.
In this talk, the speaker will demonstrate how to use both local and cloud-hosted ML models to transform recorded Latin audio into structured plaintext that encodes not only the text itself, but also its morphological and semantic context. Once transformed, this text can be rendered with attached metadata that enables powerful word-at-point features, such as linguistic details in the echo area, accurate dictionary lookups, and audio playback with precise word alignment.
Target Audience: