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.
