AI Language Learning App

Pronunciation feedback that is actually useful

"Try again" is not feedback. A learner needs to know which sound was wrong and what to do with their mouth instead.

That requires phoneme-level assessment rather than plain transcription — comparing the produced sounds against expected ones and scoring each. It also requires knowing which errors matter.

Making conversation feel like conversation

The technical bar is latency. A human pause in dialogue is under a second; a two-second gap while a server thinks turns practice into an interview with a bad phone line.

Getting there means streaming speech recognition rather than waiting for the learner to finish, generating the response as the recognition completes, and streaming synthesised speech out as it is produced instead of waiting for the full audio.

The other half is behavioural. The partner should interrupt occasionally, use filler words, ask follow-up questions and stay in a consistent character. A partner that responds in perfectly formed paragraphs teaches a register nobody speaks in.

Correction that does not kill the conversation

Correct every error immediately and the learner stops speaking. Correct nothing and errors calcify. The balance is a designed behaviour, not a model setting.

• Let small errors pass during flow. Note them silently and address them afterwards.
• Recast rather than interrupt. The learner says something wrong; the partner naturally repeats it correctly in its reply.
• Stop only for meaning breakdowns. If the error made the sentence incomprehensible, that warrants an interruption.
• Review at the end. Three things to work on after the conversation, with the learner's own audio to listen back to.
• Track error types, not error counts. A learner consistently dropping articles has one problem, not forty.

Which market to build for

The obvious framing is Indians learning foreign languages. The larger and more reachable market is Indians improving their English for work — professionals who read and write competently but freeze in a client call, freshers preparing for placement interviews, people preparing for IELTS.

That audience has a concrete outcome to buy, an employer or a deadline creating urgency, and willingness to pay accordingly. Casual language learning competes against very good free apps with enormous content libraries.

Second-strongest is regional language acquisition within India — someone relocating to Bengaluru or Chennai who wants functional Kannada or Tamil. It is underserved, and the AI partner approach suits it well because the goal is speaking, not certification.

The content behind the conversations

Free-form chat is not a curriculum. Learners need scenarios with objectives — order food, handle a client objection, describe a technical problem, get through an interview — each with target vocabulary and a difficulty level.

We build a scenario framework where each has a goal, expected phrases, a success condition and a difficulty band, then let the model improvise within it. That gives the learner a sense of progress that open chat never provides, and gives you a content pipeline your team can extend without engineering.

Levels should be tied to something recognised, CEFR or an equivalent, so a learner can say where they are and see themselves move.

What the build includes

The app for iOS with real-time speech infrastructure, the pronunciation assessment layer, the scenario framework as editable content, progress tracking against levels, subscription billing, and full source.

We include the per-minute cost model from the pilot. Real-time voice is the most expensive thing in this app by a wide margin, and knowing what a heavy user costs before you set the price is the difference between a good business and a busy one.