500 hours of Punjabi 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 Punjabi specifically: Punjabi is tonal: high, low and level tones distinguish words, and tone is not marked in Gurmukhi orthography
• Code-mixing handled explicitly rather than edited out — Punjabi speech mixes Hindi and English freely, with strong diaspora influence in urban registers.

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.

Cover all three historic regions (Majha, Malwa, Doaba); tone realisation differs measurably between them.

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.

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.

• Punjabi is tonal: high, low and level tones distinguish words, and tone is not marked in Gurmukhi orthography
• Punjabi speech mixes Hindi and English freely, with strong diaspora influence in urban registers.
• Tonal variation is essentially unmodelled in public Punjabi data, and Malwai/Doabi rural speech is scarce.

• 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
• Tone is unrepresented in text, so pronunciation lexicons must be built from audio, not from spelling
• Shahmukhi vs Gurmukhi script decisions must be fixed per project

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

• 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: 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
• Punjabi carries 5 recognised varieties across Punjab, Haryana, Delhi, so the quota matrix is wider than the headline volume suggests
• Tonal variation is essentially unmodelled in public Punjabi data, and Malwai/Doabi rural speech is scarce.

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