标签：#barbados

Automatic speech recognition is very good until somebody mentions the name of a local school, a village, a politician, a festival, or a cricket ground. Then things get strange. In an earlier test with audio from Barbados, GPT Transcribe and GPT Audio 1.5 heard the event name “Rise Together” as “Rice Together”, while Qwen3.5-Omni Plus and Flash got it right. Those are different models from the Qwen3-Omni checkpoint used here, but the result motivated this experiment. Acoustically, the mistake is understandable. Culturally, it is wrong. A person who knows the local context has another signal available: they know that Rise Together is the plausible name. That led me to a question: can we give an audio-native model a stronger model of Barbados, using text that already contains the names, institutions, places, events and relationships it is likely to hear? So I took an archive of Barbados newspapers, turned it into 51.6 million tokens, and used it for domain-adaptive pretraining of the Thinker inside Qwen3-Omni. The result is promising, but not conclusive. The adapted model produced higher scores on our preliminary Barbados knowledge probe, particularly on people and institutions. It also got slightly worse on a small set of general-knowledge controls. And, most importantly, we have not yet shown that it transcribes audio more accurately. This is a very preliminary result. It came from our first training run, which we stopped at step 500 of a planned 801 steps. We were also still extracting the newspaper archive, so the 51.6 million training tokens represent the material available for that run rather than the full corpus we ultimately intend to use. This post is about what we have actually demonstrated, what broke along the way, and why I think the experiment is still worth pursuing. The Problem Is Not Just Acoustic A transcription model is doing more than converting sound into letters. When audio is clean and a word is common, the acoustic evidence can be enough. But re