Buying data for low-resource languages: what changes
For a language with no existing corpus, everything about the project is different — starting with the fact that the timeline is set by speaker recruitment, not recording.
Low-resource does not mean few speakers. Navajo has fewer than 200,000 speakers, but so do several languages with well-developed digital corpora. What makes a language low-resource is the absence of existing text and audio that a model can learn from.
For a buyer, that changes the shape of the project more than any other single factor.
The bottleneck moves
In a well-resourced language, the constraint is usually studio time and annotation throughput. Speakers are easy to find and the recruitment step is a formality.
In a low-resource language the constraint is almost always recruitment. Qualified native speakers may be geographically concentrated, may not be reachable through standard channels, and may have limited availability. Finding forty of them can take longer than recording all of them.
This is why timelines for low-resource projects are unreliable in a specific way: the estimate is dominated by a step whose duration is hard to predict in advance.
Existing text is not a shortcut
When a language has some written material — religious texts, colonial-era documentation, community publications — there is a temptation to use it as the transcription target.
It usually does not work. Older written material tends to be in a register nobody speaks, in an orthography that has since changed, or in a dialect other than the one you are recording. The audio and the text end up describing different things.
What to expect on quality
The realistic quality target for a first collection in a low-resource language is lower than for a well-resourced one, and that is a property of the situation rather than of the producer.
- Annotator agreement will be lower, because the orthography may not be fully standardized and there is no established convention to fall back on.
- Some of the annotation budget will go to establishing conventions rather than applying them.
- A pilot batch matters more here than anywhere else, because it is where the conventions get tested.
The offsetting advantage
Low-resource data is scarce, which means it is also defensible. A competitor cannot buy the same corpus from a marketplace, because there is no marketplace listing for it.
For teams building a capability that depends on language coverage, that scarcity is the point. The project is slower and more expensive per hour, and the resulting asset is one that cannot be replicated by writing a larger purchase order.