Living Speech: Building the First N'Ko ASR for 14

Every speech recognition system for Bambara outputs Latin characters. French colonial characters designed for French colonial administrators. Not for the 40 million people who actually speak and read these languages. This is the story of how a $14 experiment on consumer hardware produced a speech recognition system that competes with models 38 times its size. And why Solomana Kante's 1949 design decisions turned out to be a machine learning advantage nobody predicted. There is no N'Ko speech corpus. Every Bambara audio dataset has Latin transcriptions. The FarmRadio datasets, the MALIBA-AI models, the Bayelemabaga corpus. All Latin output. If you want to build a system that listens to Bambara and writes N'Ko, you first need to solve a problem that nobody has needed to solve before: convert thousands of Latin Bambara transcriptions into valid N'Ko. Latin Bambara was designed by French colonial linguists in the 20th century. It reflects French phonological conventions, not Manding phonological reality.