Free Indian English speech data sample

• Indian English audio from at least four speakers with a mixed gender and age spread
• Coverage of North Indian (Hindi-substrate), Maharashtrian, South Indian (Tamil/Telugu/Kannada/Malayalam substrate) where your spec calls for dialect breadth
• Verbatim transcripts in Latin, with a romanised parallel transcript on request
• Per-file metadata: speaker ID, age band, gender, district, device, environment and SNR
• A short QA report showing how the files were checked before they were sent

The sample is only useful if it looks like the data you would actually buy. Send the spec you would put in a purchase order and we record against that.

• Run it through your existing ASR or TTS pipeline and read the error breakdown by speaker and dialect
• Check how Indian English code-mixing is transcribed: Indian English embeds Hindi and regional discourse markers, kinship terms, and food and place vocabulary that Western English lexicons lack.
• Check the transcription convention against your tokeniser — Indian-specific vocabulary flagged as errors by spellcheck-driven QA is the usual failure point
• Confirm the metadata is complete enough to filter and stratify by

Indian English has roughly 130 million speakers across Pan-India, with distinct regional accent bands, and models trained on generic multilingual data typically fail on the same things each time.

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.

• Phonetics your acoustic model may not have seen: Retroflex realisation of /t/ and /d/
• Utterance types worth sampling separately: Accent-band balanced read speech with substrate tags, Business and support calls in English, Indian names, addresses, PIN codes and lakh/crore amounts
• Recording geography in the sample: Bengaluru, Delhi, Mumbai

Nothing automatic. If the sample works, we scope the full build against the same specification and quote a fixed price. If it does not, tell us what failed — that feedback is more useful to us than a polite no.

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