How much training data do you need for ivr & voice bots?

What the model actually needs

Deploying automated telephony flows that hold up against real Indian callers on narrowband lines.

• Telephony-bandwidth audio
• Dual-channel calls
• Intent-labelled utterances against a live taxonomy

Metrics that tell you when you have enough

Collect against a metric, not against a number of hours. When a pilot batch moves the metric and a second batch of the same profile moves it less, you are at the point where composition, not volume, is the constraint.

• Intent accuracy
• Containment rate
• Barge-in handling

Common mistakes

• Studio audio downsampled to fake telephony
• Scripted callers who never interrupt
• Intent sets written by product, not derived from real calls

A sensible collection sequence

Services that supply this data

This use case is normally served by call centre speech data, conversational speech data, audio annotation. Most programmes combine two of them, because raw collection without matched annotation rarely moves an applied metric on its own.

Hold back an honest evaluation set

Reserve deployment-condition data that never enters training. Teams that evaluate on data recorded in the same sessions as their training data consistently overestimate real-world performance, then discover the gap after launch.