500 hours of Indian English Telephony Speech

Speech captured over a real telephony path — a genuine call through the network, not studio audio downsampled afterwards to imitate one.

Scenario-driven calls between a caller and an agent or IVR flow, each leg recorded separately, across a deliberate spread of handsets and network conditions.

Yield at this style: 50–60% of recorded time is accepted. Dropped calls, network artefacts beyond tolerance and unusable legs are discarded. Real network conditions are the point of the style and also its main cost.

• Call scenarios drawn from real contact-centre intents: balance enquiry, complaint, booking change, escalation
• IVR flows scripted with deliberate mis-entry and barge-in paths, since those are where deployed systems fail
• Handset spread specified up front — low-end Android, feature phone, landline — because handset variance is a real acoustic axis
• Background conditions varied on purpose: street, vehicle, indoor, since real callers are rarely in quiet rooms
• Built against Indian English specifically: Retroflex realisation of /t/ and /d/
• Code-mixing handled explicitly rather than edited out — Indian English embeds Hindi and regional discourse markers, kinship terms, and food and place vocabulary that Western English lexicons lack.

Eight to twelve weeks. Cities are staggered rather than started together, so the protocol corrections found in the first city are applied before the last one begins recording.

Six to eight batches, fortnightly, each speaker-disjoint so you can train on early batches without contaminating a later evaluation split.

Balance by substrate language, not by city alone, and tag each speaker so accent-band evaluation is possible after delivery.

At 500 hours quotas are enforced per dialect and reconciled fortnightly. Aggregate demographics are reported per batch so drift is visible while there is still time to correct it.

Recruitment must be stratified by handset and carrier as well as by dialect, which adds a screening axis the other styles do not have.

• Retroflex realisation of /t/ and /d/
• Indian English embeds Hindi and regional discourse markers, kinship terms, and food and place vocabulary that Western English lexicons lack.
• Commercial English ASR is trained overwhelmingly on US and UK speech. Indian English accent data with substrate-language tagging is the fastest way to close the accuracy gap for Indian deployments.

• Codec and sample rate verified per file, rejecting any studio audio that has been downsampled to fake a telephony path
• Echo and double-talk checked on both legs
• DTMF events verified against the call log
• Level normalisation applied per leg, since handset output levels vary far more than studio microphones do
• Indian-specific vocabulary flagged as errors by spellcheck-driven QA
• Numbers spoken in lakhs and crores mis-normalised into millions

100% technical QA, 10% content QA stratified by city, dialect, transcriber and recording condition, with automatic escalation to 100% on any failing batch.

• Studio audio downsampled to 8 kHz and passed off as telephony, which has none of the codec or packet-loss characteristics that matter
• Echo and double-talk that make the agent leg unusable
• Carrier and handset monoculture, producing a corpus that only represents one acoustic path
• Over-clean recordings from participants who move somewhere quiet to take the call, defeating the purpose

Cost at this band is driven by: Field and rural capture ratio — mobile rig hours cost more than studio hours; Rare dialect quotas, where the last 10% of the cohort can cost as much as the first 40%.

• Speaker duplication across cities becomes a real risk at this cohort size and needs active de-duplication against voice and ID
• Rural capture depends on weather and travel in a way studio work does not, so mobile-rig batches carry schedule variance
• Quota drift compounds across six cities unless demographics are reconciled weekly rather than at the end
• Indian English carries 5 recognised varieties across Pan-India, with distinct regional accent bands, so the quota matrix is wider than the headline volume suggests
• Commercial English ASR is trained overwhelmingly on US and UK speech. Indian English accent data with substrate-language tagging is the fastest way to close the accuracy gap for Indian deployments.

• WAV audio to your naming convention, with the per-file manifest
• Verbatim Latin transcripts with utterance-level timestamps
• Call metadata: duration, codec, handset class, carrier, packet-loss events
• Intent label per call and per turn
• DTMF and hold, transfer and barge-in events
• Agent and caller leg identifiers
• Per-speaker metadata: age band, gender, region, dialect, education band
• Consent records mapped to speaker IDs
• QA report with pass rates, rejection reasons and agreement statistics
• Speaker-disjoint train / dev / test splits on request

• Contact-centre and IVR ASR
• Voice bots operating over the phone network
• Intent classification on narrowband audio
• Robustness to codec and packet loss

Narrowband telephony audio is the wrong input for TTS or any wideband model — the frequency content simply is not there. Use it for models that will be deployed on a phone line and nothing else.