100 hours of Tamil 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 Tamil specifically: Extreme diglossia: written Tamil and spoken Tamil differ so much that read-speech corpora are near-useless for conversational ASR
• Code-mixing handled explicitly rather than edited out — Tanglish is the default urban register. Technology, finance, and workplace vocabulary is largely English embedded in Tamil syntax.

Four to six weeks. Two cities running in parallel means recruitment and recording overlap rather than queue behind each other.

Three batches at roughly two-week intervals, each one a self-contained, speaker-disjoint slice you can evaluate independently.

Recruit by district rather than by city alone; Chennai-only cohorts produce models that degrade sharply in the south and west of the state.

At 100 hours the cohort is deliberately simplified: two or three dialect groups rather than the full spread, with quotas enforced in aggregate. A build this size cannot support per-cell targets and should not claim to.

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

• Extreme diglossia: written Tamil and spoken Tamil differ so much that read-speech corpora are near-useless for conversational ASR
• Tanglish is the default urban register. Technology, finance, and workplace vocabulary is largely English embedded in Tamil syntax.
• Almost all public Tamil audio is literary read speech from news or scripture. Genuine colloquial Tamil, especially southern and Kongu varieties, is the single biggest gap for anyone building Tamil voice products.

• 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
• Transcribers normalising spoken Tamil into literary Tamil, destroying the acoustic-text alignment
• ழ / ள / ல confusion

100% technical QA, 25% content QA, escalating to full review on any batch that fails the agreed error threshold.

• 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: Dialect spread is the main driver at this band — two contrasting cities cost more than two convenient ones; Turnaround compression, if you need it inside four weeks.

• Two-city cohorts can hide a dialect gap that only appears when the model meets a third region
• Transcription consistency between two city teams needs an explicit convention document or the batches will not match
• Tamil carries 6 recognised varieties across Tamil Nadu, Puducherry, parts of Karnataka and Kerala, so the quota matrix is wider than the headline volume suggests
• Almost all public Tamil audio is literary read speech from news or scripture. Genuine colloquial Tamil, especially southern and Kongu varieties, is the single biggest gap for anyone building Tamil voice products.

• WAV audio to your naming convention, with the per-file manifest
• Verbatim Tamil 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.