What should speech ai companies know before buying Indian speech data?

The problems that recur

Teams whose core product is speech recognition or synthesis, where dataset quality is the product roadmap and word error rate is the metric everyone watches.

• WER on Indian languages is dominated by dialect and code-mixing failures that generic corpora do not cover
• Public Indic corpora are read speech and do not transfer to spontaneous production audio
• Benchmark sets leak speakers into training splits, inflating reported accuracy

How to evaluate a data partner

Ask for a paid pilot delivered in your ingest format before volume. A vendor who cannot produce 10 hours to spec will not produce 1,000 to spec, and the pilot cost is trivial against the cost of discovering the mismatch late.

• Are train/dev/test splits speaker-disjoint by construction?
• Is transcription verbatim, with disfluencies preserved?
• Is per-token language ID available for code-mixed speech?
• Is inter-annotator agreement measured and reported?

Contract terms to insist on

Consent language must explicitly cover commercial AI model training and the term of use. Generic recording releases do not, and a corpus with defective consent is unusable regardless of its audio quality.

• Speaker-disjoint splits guaranteed contractually
• Right to publish benchmark results
• Re-record remedy for QA failures

A typical engagement

500-2,000 hours in one flagship language plus tagged benchmark sets per dialect.

Scope is fixed in writing, priced fixed against that scope, piloted, then scaled with rolling batch delivery and weekly reporting so training is not blocked on a single final handover.

Services this segment usually buys

Most programmes in this segment combine asr training data, tts training data, conversational speech data. Collection alone rarely solves the problem, because the annotation layer is what makes the audio trainable.