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Vaaani
The speaking coach that measures your voice, not guesses it
Vaani is an 18-layer acoustic pipeline for IELTS/TOEFL speaking. It measures your formants with Praat, your rhythm with peer-reviewed metrics, your grammar with syntax trees — then cross-validates all three against 8 calibrated Indian L1 profiles. No LLM in the scoring loop. No GPU. Same audio = same band, every time. When 2+ layers agree on L1 interference, confidence goes up. When they don't, it's flagged. Vaani tells you what it measured, how confident it is, and what it couldn't measure.
Hi Product Hunt — Neil Shankar here, the maker.
I'm an applied linguist, and Vaani started from a frustration I kept seeing in the IELTS / TOEFL prep market: every speaking-prep app gives Indian candidates a single pronunciation number and a vague "work on your accent" hint. Nothing in the pipeline knows their L1.
A Bengali speaker whose /θ/ slips to /t/ has a different articulatory fix than a Hindi speaker whose retroflex /ʈ/ bleeds into English /t/ — but the generic scorer treats both as the same mistake. So Vaani measures your voice the way a phonetician would in a lab: Praat for formants and voice quality, two-pass per-speaker pitch tracking, rhythm metrics, Whisper for word-level transcription. Those measurements are compared against an L1-specific acoustic fingerprint, and the report names the exact transfer pattern that produced your band plus the articulatory adjustment that would close the gap. Every band traces back to a measurement you can see in your own report. No LLM in the band-mapping loop — same audio, same band, every time.
A few honest constraints I want surfaced on launch day rather than buried:
1. Pronunciation band only. Fluency, Lexical Resource, and Grammatical Range require a human examiner and are explicitly marked "Not scored" on every report. The refusal is the design, not a missing feature.
2. Six L1 profiles — Bengali, Hindi, Tamil, Telugu, Marathi, Gujarati. Bengali and Hindi attractors are empirically calibrated against the Svarah corpus (AI4Bharat, IIT Madras); the other four use published L2-phonetics literature as the prior and are flagged as such on every report.
3. Bengali ground truth is cross-referenced against Asoke Kumar Datta's Acoustics of Bangla Speech Sounds (ISI Kolkata / Springer 2017) — the canonical acoustic-phonetic record for Standard Colloquial Bengali.
4. Not an official score. No affiliation with Cambridge, IDP, British Council, or ETS. A diagnostic instrument, not a substitute for an examiner.
What evolved over the build: I started with a full four-criterion IELTS rubric and stripped it back to acoustic-core Pronunciation-only after a calibration round showed me that honest reporting beats feature parity. The engine also moved from a GPU-leaning phoneme-alignment stack to a CPU-only Praat-centric one — slower per submission, but more reliable on real Indian-accented audio and far easier to deploy.
I'd love feedback from anyone prepping for IELTS / TOEFL Speaking, anyone running a coaching centre in tier-2/3 India, or phonetics-literate folks willing to pressure-test the methodology. Critical questions especially welcome.
— Neil Shankar Ray
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About Vaaani on Product Hunt
“The speaking coach that measures your voice, not guesses it”
Vaaani was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #158 on the daily leaderboard. Vaani is an 18-layer acoustic pipeline for IELTS/TOEFL speaking. It measures your formants with Praat, your rhythm with peer-reviewed metrics, your grammar with syntax trees — then cross-validates all three against 8 calibrated Indian L1 profiles. No LLM in the scoring loop. No GPU. Same audio = same band, every time. When 2+ layers agree on L1 interference, confidence goes up. When they don't, it's flagged. Vaani tells you what it measured, how confident it is, and what it couldn't measure.
Vaaani was featured in Analytics (172k followers), Education (78.6k followers) and Languages (14.3k followers) on Product Hunt. Together, these topics include over 47k products, making this a competitive space to launch in.
Who hunted Vaaani?
Vaaani was hunted by NEIL SHANKAR ROY. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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NEIL SHANKAR ROY