Re-voicing an existing training video in another Indian language
An induction session or an equipment walkthrough recorded once is transcribed, translated and re-voiced with synthetic speech timed against the original, so a single recording serves sites that do not share a working language. Somebody who speaks the target language signs the cut off before it is published.
- Effort
- Weeks of work
- Skill level
- Some technical skill
- Organisation size
- Mid-market
- Value
- Time saved, Quality
Tools named for this
- Speech recognition covering the languages scheduled in the Constitution of India
- An open translation model that spans the same set of languages
- A speech synthesiser with a neutral voice that is not built to imitate the original presenter
- A named reviewer for each target language, with authority to block publication
What to check before you ship it in India
- The IT Rules as amended in February 2026 keep outside 'synthetically generated information' any use of a computer resource solely for improving accessibility, clarity, quality, translation, description, searchability or discoverability, without generating, altering or manipulating any material part of the underlying material. A straight translated voice-over can sit inside that proviso. Re-timing the speaker's lips to the new audio, or generating a voice meant to be taken for the original presenter's, alters a material part and puts the output back inside the definition.
- A presenter's face and voice are their personal data. Where the presenter is a current employee the likely basis is the section 7(i) legitimate use for employment purposes rather than consent; either way the processing stays bound to the specified purpose, so a recording made for one induction video is not a licence to synthesise that person saying something they never said — least of all once they have left the organisation.
- Nobody downstream can be assumed able to tell. The research community that works on separating genuine speech from synthetic speech runs its evaluations without matched training data precisely because, in its organisers' own framing, the nature of such speech can never be anticipated with confidence.
Sources
Every claim on this page traces to one of these, on the date it was read.
- IndicVoices-R: Unlocking a Massive Multilingual Multi-speaker Speech Corpus for Scaling Indian TTS · Sankar, Anand, Varadhan, Thomas, Singal et al., AI4Bharat (NeurIPS 2024, Datasets and Benchmarks track) · that this is done · read 2026-09-01
- The Digital Personal Data Protection Act, 2023 (No. 22 of 2023) — most obligations commence 13 May 2027 under the DPDP Rules 2025 — s.6(1) · Ministry of Electronics and Information Technology · a rule · read 2026-09-01
- ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection · Yamagishi, Wang, Todisco, Sahidullah, Patino, Nautsch, Liu, Lee, Kinnunen, Evans and Delgado (ASVspoof 2021 Workshop) · how it is done · read 2026-09-01
- Benchmarking Speech-to-Speech Translation Models (COMPASS) · Koudounas, Futami, Jodelet, Take, Watanabe and Tsunoo (arXiv preprint, 2 June 2026) · how it is done · read 2026-09-01
- The Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021, as amended by G.S.R. 120(E) dated 10 February 2026 — r.2(1)(wa) · Ministry of Electronics and Information Technology, Government of India · a rule · read 2026-09-01
- Dubbing in Practice: A Large Scale Study of Human Localization With Insights for Automatic Dubbing · Brannon, Virkar and Thompson (Transactions of the Association for Computational Linguistics, vol. 11, pp. 419-435, 2023) · how it is done · read 2026-09-01
- IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages · Gala, Chitale, Raghavan et al., AI4Bharat (Transactions on Machine Learning Research, 2023) · how it is done · read 2026-09-01