100 speakers 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.

Three to four weeks, almost entirely determined by how narrow the screening criteria are.

A single delivery with the full demographic report, since the cohort report is the point of a build this size.

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

At 100 speakers 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.

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% content QA, plus per-speaker demographic verification against the screening record.

• 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: Screening narrowness dominates; the recording itself is a minor cost at this size.

• Narrow criteria can make a cohort unrecruitable at any price, which is exactly what this band exists to find out
• 100 speakers cannot support per-dialect evaluation — treat the results as directional
• 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.