250 hours of Indian English Scripted Speech

Speakers read prompts from a phonetically balanced script shown one line at a time, with retakes on any flubbed or misread line.

A 45-minute booth session yields roughly 300–400 prompts. One file per utterance, cut at the prompt boundary, so alignment is exact before any forced-alignment pass runs.

Yield at this style: 85–90% of recorded time is accepted. Prompt-level retakes catch problems inside the session, so very little is discarded afterwards. This is the highest-yield style we run.

• Script built for triphone coverage rather than word coverage, so rare phone contexts appear often enough to train on
• Digit strings, dates, currency and person names blocked separately, because these are where deployed ASR actually fails
• Domain lexicon injected from your product vocabulary when you supply one
• Sentence-length distribution spread deliberately, since all-short prompts produce a model that cannot handle long utterances
• Built against Indian English specifically: Retroflex realisation of /t/ and /d/
• Code-mixing handled explicitly rather than edited out — Indian English embeds Hindi and regional discourse markers, kinship terms, and food and place vocabulary that Western English lexicons lack.

Six to eight weeks. Beyond three cities the bottleneck moves from recruitment to transcription throughput, which is why the transcriber count rises faster than the engineer count.

Four to five batches on a fortnightly cadence, sequenced so the dialects you care about most land first.

Balance by substrate language, not by city alone, and tag each speaker so accent-band evaluation is possible after delivery.

At 250 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.

Requires literate speakers comfortable reading aloud in the target script, which is the main constraint on cohort breadth and has to be actively counterweighted.

• Retroflex realisation of /t/ and /d/
• Indian English embeds Hindi and regional discourse markers, kinship terms, and food and place vocabulary that Western English lexicons lack.
• Commercial English ASR is trained overwhelmingly on US and UK speech. Indian English accent data with substrate-language tagging is the fastest way to close the accuracy gap for Indian deployments.

• Prompt-to-audio match verified by a native reviewer; a misread line is a rejected file, not an edited transcript
• Hyperarticulation flagged — a speaker over-enunciating because they are reading produces audio that does not match deployment
• Clipping and truncation checked at both utterance boundaries
• Per-speaker prompt coverage confirmed, so no speaker silently skips a block
• Indian-specific vocabulary flagged as errors by spellcheck-driven QA
• Numbers spoken in lakhs and crores mis-normalised into millions

100% technical QA, 15% content QA with stratified sampling across city, dialect and transcriber, so no single team's output goes unchecked.

• Reading voice: flat prosody and unnatural stress that trains a model on speech nobody actually produces
• Prompt fatigue in the back half of long sessions, where accuracy drops and pace flattens
• Speakers who are not fluent readers, which quietly biases the cohort toward higher education bands
• Script leakage across speakers, producing a corpus that memorises sentences instead of covering sounds

Cost at this band is driven by: Transcription depth — verbatim with disfluencies costs materially more than clean-read transcription; Number of distinct dialect quotas rather than raw hours.

• Transcription becomes the critical path and stays there for the rest of the volume bands
• Coordinator turnover mid-build causes quota drift that is only visible in the final demographic report unless it is audited weekly
• Indian English carries 5 recognised varieties across Pan-India, with distinct regional accent bands, so the quota matrix is wider than the headline volume suggests
• Commercial English ASR is trained overwhelmingly on US and UK speech. Indian English accent data with substrate-language tagging is the fastest way to close the accuracy gap for Indian deployments.

• WAV audio to your naming convention, with the per-file manifest
• Verbatim Latin transcripts with utterance-level timestamps
• Prompt ID mapped to every utterance
• Verbatim deviation flags where the speaker departed from the script
• Per-utterance SNR and duration in the manifest
• 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

• ASR acoustic model baselines
• TTS voice building where a single speaker is recorded at depth
• Pronunciation lexicon and G2P validation
• Forced-alignment and phone-boundary work

Scripted audio contains no disfluencies, no false starts and no natural turn-taking. A model trained on it alone degrades sharply on real spontaneous input, so it is a baseline layer rather than a complete training set.