50 hours of Malayalam Spontaneous Speech

Speakers talk unscripted on prompted topics — describing a process, recounting an event, arguing a position — with a moderator who prompts but does not lead.

A 60-minute session covering six to eight topics, recorded continuously and segmented afterwards into utterances at natural pause boundaries.

Yield at this style: 60–70% of recorded time is accepted. Long silences, moderator speech and abandoned topics are cut in post, so recorded hours run well ahead of delivered hours. Budget for the gap.

• Topic banks graded by familiarity, so speakers across education and occupation bands all have something to say
• Open prompts only — anything answerable with yes or no produces thirty seconds of audio and a stalled session
• Culturally grounded topics per region, since a prompt that works in Mumbai can draw blank looks in Guwahati
• Topic rotation across the cohort so the corpus does not over-represent a handful of subjects
• 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 from signed specification. Recruitment is the critical path, not recording — the studio time itself is under two weeks.

Two batches: a 10-hour first batch in week two so you can run it through your pipeline while the rest is still recording, then the balance on completion.

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

At 50 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.

Screening is for willingness to talk, not literacy, which opens the cohort to speakers a scripted protocol would exclude. Expect to over-recruit by 15% for speakers who freeze on the day.

• 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.

• Segment boundary review — bad boundaries produce truncated words that poison training more than they help
• Disfluency transcription consistency audited across transcribers, since conventions drift fast on this style
• Moderator speech confirmed absent from delivered segments
• Topic distribution checked per speaker so one dominant subject does not skew the language model
• 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. At this volume there is no reason to sample, and a pilot exists precisely to surface specification problems.

• Speakers who dry up after a minute, leaving sessions that look complete by duration but are mostly silence
• Drift into a reading register when a speaker becomes self-conscious about the microphone
• Moderator over-prompting, which turns a monologue corpus into an interview corpus
• Transcriber normalisation — quietly cleaning up disfluencies destroys the exact signal this style exists to capture

Cost at this band is driven by: Fixed setup cost is amortised over very few hours, so the per-hour rate is at its highest here; Narrow demographic requirements bite hardest at small cohort sizes.

• The specification itself is usually the risk, not delivery — pilots exist to find the clauses that do not survive contact with real speakers
• A single-city cohort will not represent the language nationally, and reading pilot results as national is the most common mistake at this band
• 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
• Filled-pause and false-start markers
• Segment offsets into the parent session file
• Topic label per segment
• Speech-rate and pause-density statistics per speaker
• 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

• Robust ASR for real user speech
• Language modelling on natural syntax
• Disfluency detection and removal
• Prosody and speech-rate modelling

Phonetic balance cannot be guaranteed — speakers say what they say. If you need specific phone contexts or a controlled lexicon, this style has to be paired with a scripted layer.