1,000 hours of Malayalam Conversational Speech

Two speakers hold an unscripted conversation seeded with a scenario, recorded on separate channels so overlap and turn-taking survive into the delivered files.

A 45–60 minute paired session with both speakers on isolated microphones, either in adjacent treated rooms or split-mic in one room with bleed measured and logged.

Yield at this style: 55–65% of recorded time is accepted. Overlap regions, crosstalk bleed and one-sided stretches all cost delivered time. The lowest-yield studio style we run, and priced accordingly.

• Scenario seeds rather than scripts — a disagreement to resolve, a plan to make, an experience to compare
• Pairing designed deliberately: familiar pairs produce natural interruption, stranger pairs produce polite turn-taking, and you need both
• Scenarios that invite disagreement, because agreeable conversation produces almost no overlap to train on
• Register mixed across pairs so the corpus is not uniformly formal
• Built against Malayalam specifically: One of the most consonant-dense Indian languages; long geminates and clusters raise word error rates sharply
• Code-mixing handled explicitly rather than edited out — Manglish is standard in urban and professional speech, with heavy English noun and verb insertion.

Twelve to sixteen weeks. This is a programme with its own governance rather than a project — weekly demographic reconciliation, a standing protocol review and a named counterpart on your side.

Ten to twelve batches, fortnightly, with a formal acceptance test per batch. Rejected batches are re-recorded rather than patched, so schedule contingency is built into the plan.

Budget higher transcription effort per audio hour for Malayalam than for Hindi; speech rate and morphology make it slower to annotate.

At 1,000 hours the quota matrix is enforced per cell, not in aggregate. Every dialect, age and gender combination carries its own target and is signed off individually before final acceptance, because an aggregate 50/50 split can hide a cell that was never filled at all.

Effectively doubles recruitment load, since speakers are booked in matched pairs and a single drop-out cancels the whole session. Plan on 20–25% over-recruitment.

• One of the most consonant-dense Indian languages; long geminates and clusters raise word error rates sharply
• Manglish is standard in urban and professional speech, with heavy English noun and verb insertion.
• Central Kerala news-reading dominates public data. Malabar and southern varieties, and fast conversational speech generally, are missing.

• Channel bleed measured on every session and rejected above the agreed threshold
• Diarisation labels verified against the isolated channels rather than inferred from the mix
• Turn boundaries and overlap regions checked by a native listener
• Speaking-time balance per pair audited, so a dominant speaker does not silently halve the session's value
• Old vs new script (chillu characters, Unicode normalisation) mixed within a dataset
• Fast speech leads to dropped-word transcription errors without a second-pass QA

100% technical QA, 7% content QA stratified across every axis, plus a blind 1% re-transcription audit measuring inter-annotator agreement across the whole programme.

• Crosstalk bleed that makes clean per-speaker training targets impossible to recover afterwards
• One speaker dominating, leaving a pair that delivers half the expected audio
• Unnatural politeness between strangers, producing clean but unrepresentative turn-taking
• Scheduling attrition — both speakers have to show up, so no-show rates compound rather than add

Cost at this band is driven by: Programme management overhead, which is real at this scale and should be quoted explicitly rather than hidden in the hourly rate; The tail of rare dialect and demographic quotas.

• Transcriber consistency across a 45-person team is the dominant quality risk and needs continuous calibration, not a one-time briefing
• Speaker pool exhaustion in smaller cities, where the genuinely available cohort is finite
• Specification drift over three months as your model team learns what it actually needs — build a change-control step in rather than pretending it will not happen
• Malayalam carries 5 recognised varieties across Kerala, Lakshadweep, Puducherry (Mahe), so the quota matrix is wider than the headline volume suggests
• Central Kerala news-reading dominates public data. Malabar and southern varieties, and fast conversational speech generally, are missing.

• WAV audio to your naming convention, with the per-file manifest
• Verbatim Malayalam transcripts with utterance-level timestamps
• Speaker-turn segmentation with start and end timestamps
• Overlap regions marked with participating speaker IDs
• Backchannel and interruption markers
• Per-pair relationship metadata: familiar or stranger
• 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

• Speaker diarisation
• Meeting and multi-party ASR
• Turn-taking and endpointing for voice agents
• Speaker separation and target-speaker extraction

Two-party conversation does not generalise to multi-party meetings with four or more speakers, where overlap statistics change substantially. Specify that case separately.