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

The problems that recur

Voice-bot and chat-plus-voice platforms deploying into Indian markets, where the gap between demo accuracy and live accuracy is a code-mixing problem.

• Bots trained on clean single-language data fail on real switching mid-utterance
• Barge-in, overlap and background noise are absent from scripted training data
• Intent coverage does not match the messy way Indian users actually phrase requests

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.

• Does the data include overlap, interruptions and backchannels?
• Are utterances collected over the same channel conditions as production?
• Is intent labelling done against your live taxonomy?

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.

• Scenario confidentiality
• Right to reuse across bot versions
• Delivery in a format that drops into an existing pipeline

A typical engagement

100-400 hours of scenario-driven conversational and telephony audio per language.

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 conversational speech data, call centre speech data, asr training data. Collection alone rarely solves the problem, because the annotation layer is what makes the audio trainable.