Market analysis
Why do AI models underperform on Indian languages?
Updated 2026-08-01 · 4 min read
Short answer
Indian-language models underperform mainly because of data, not architecture. Public corpora for most Indian languages are small, read-speech heavy, urban-skewed and rarely code-mixed, while real usage is conversational, dialectally varied and heavily mixed with English. Add script complexity, inconsistent transcription conventions across sources, and evaluation sets drawn from the same narrow distribution as training, and a model that looks strong on benchmarks fails on a call from a semi-urban user. The fix is targeted collection: spontaneous and conversational audio, dialect quotas that match your users, code-mixed material, and a held-out evaluation set recorded under deployment conditions.
Key takeaways
- The gap is distributional: training data does not look like real usage.
- Code-mixing and dialect variation are the two largest unmodelled factors.
- Benchmarks drawn from the same narrow distribution hide the problem until launch.
The public data problem
Most freely available Indian-language speech is read speech from a small speaker pool in a handful of cities. It is useful for bootstrapping and misleading as a proxy for deployment audio.
Coverage is also wildly uneven: the largest languages have order-of-magnitude more public data than languages with tens of millions of speakers, and dialect coverage inside each language is thinner still.
Code-mixing
Urban Indian speech mixes English continuously, and the switched tokens are usually the content words — amounts, product names, dates — that matter most to a product. Monolingual training data systematically fails on exactly these.
Evaluation that flatters
When the evaluation set is carved from the same recording sessions as training data, it shares speakers, rooms and devices. The score is real; the generalisation it implies is not. A separate deployment-condition evaluation set usually reveals a gap of several points.
What actually fixes it
Collect spontaneous and conversational audio in the dialects your users speak, include code-mixed material at realistic density, record over the channel you deploy on, and build a frozen held-out evaluation set with per-slice reporting. In our experience this sequence moves applied metrics faster than any additional volume of read speech.
Frequently asked questions
Why do AI models underperform on Indian languages?
Indian-language models underperform mainly because of data, not architecture. Public corpora for most Indian languages are small, read-speech heavy, urban-skewed and rarely code-mixed, while real usage is conversational, dialectally varied and heavily mixed with English. Add script complexity, inconsistent transcription conventions across sources, and evaluation sets drawn from the same narrow distribution as training, and a model that looks strong on benchmarks fails on a call from a semi-urban user. The fix is targeted collection: spontaneous and conversational audio, dialect quotas that match your users, code-mixed material, and a held-out evaluation set recorded under deployment conditions.
The public data problem?
Most freely available Indian-language speech is read speech from a small speaker pool in a handful of cities. It is useful for bootstrapping and misleading as a proxy for deployment audio.
Code-mixing?
Urban Indian speech mixes English continuously, and the switched tokens are usually the content words — amounts, product names, dates — that matter most to a product. Monolingual training data systematically fails on exactly these.
Evaluation that flatters?
When the evaluation set is carved from the same recording sessions as training data, it shares speakers, rooms and devices. The score is real; the generalisation it implies is not. A separate deployment-condition evaluation set usually reveals a gap of several points.
Related reading
Turn this into a dataset specification
Tell us the languages, speaker count and minutes. You get a written scope, a protocol and a fixed price within one working day.