How does Teacher AI handle voice recognition across different accents?

Kodexo Labs built Teacher AI on a dual-provider speech-to-text architecture for accent and speech-pattern coverage. ElevenLabs scribe_v1 handles primary transcription on every learner turn; Azure Speech Services is wired in as the fallback. When scribe underperforms on a given accent, the same audio routes through Azure so the lesson does not break for the long tail of voices across the 50,000+ users in 30+ countries Teacher AI now serves.

What is the latency target for real-time feedback?

Kodexo Labs engineered Teacher AI for sub-second turn-around between learner audio in and teacher voice out. The pipeline runs ElevenLabs scribe_v1 speech-to-text, OpenAI GPT-5-mini analysis, GPT-4o-mini translation, and ElevenLabs eleven_v3 text-to-speech in parallel where order permits, with Redis Cluster caching the predictable parts of each lesson so the live turn pays only for the genuinely live computation across 30+ languages.

How does the system scale to 30+ languages?

Kodexo Labs built Teacher AI's 30+ language coverage on language-specific tooling rather than a single Latin-only pipeline. Japanese is tokenised with Kuroshiro and Chinese is segmented with jieba before the analysis layer sees them. Translation is cross-checked between OpenAI GPT-4o-mini and Google Cloud Translate, with the Google Cloud Natural Language API supplying sentence-level signal where it improves correction quality.

Can Teacher AI support corporate or B2B language training?

Kodexo Labs built Teacher AI as a consumer EdTech product, but the architecture carries into B2B with no rebuild. The voice pipeline, teacher-persona model, and progress-tracking layer are stack-shared; group provisioning, manager dashboards, and corporate billing slot onto the existing OAuth 2.0 (Google and Apple) identity layer and the Stripe plus RevenueCat payment layer the consumer product already runs.

Built for

Industry: EdTech & Learning · Delivered: AI Speaking Practice Platform

Teacher AI Reached 10,000+ Students with a Voice-First AI Speaking Coach

Real conversations with AI clones of learners' favorite YouTube language teachers. Built for intermediate learners (A2+), 24/7.

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10,000+ STUDENTS • 2M+ WORDS ACTIVATED • 500,000+ CONVERSATIONS • 30+ LANGUAGES

The problem

Generic language apps teach vocabulary. They don't teach a learner to speak.

Duolingo-style apps built huge user bases on flashcards and grammar drills. None of them solved the part that actually keeps intermediate learners stuck: real spoken conversation.

Teacher AI needed a speaking-practice platform built specifically for intermediate learners (A2+) — real AI conversation partners, not another app that drills vocabulary and calls it fluency.

Learners speak with AI clones of the YouTube teachers they already follow.

Teacher AI pairs learners with voice-cloned AI teachers for real spoken conversation practice, available 24/7 across 30+ languages.

In one screen

Three signals in one view.

Voice-clone teachers

Learners pick a YouTube teacher they already follow and start speaking.

Every persona is cloned from a real, popular language teacher. Practice sounds like that teacher, not a generic chatbot.

Real conversation, on tap

One tap and the AI teacher is listening.

Learners open a live chat with their chosen AI teacher and speak whenever they're ready. No scheduling, no waiting.

30+ languages, unlocked from day one

Pick a language, get instant access to all of them.

Every learner gets instant access to all 30+ available languages the moment they sign up. No upgrade wall to unlock another one.

Solutions We Provided

Speak any of 30+ languages, with a teacher who actually listens.
AI clones of trusted YouTube language teachers hold a real conversation, not a scripted prompt tree.
Hard corrections on demand, with the full breakdown.
Tap to see exactly what was wrong, why, and how to say it next time.
Tap any word for a translation, saved as a flashcard.
Vocabulary tracked automatically, with streaks, challenges, and a monthly leaderboard to keep learners coming back.

Soft corrections that keep the conversation moving.The AI gently rephrases what the learner said instead of stopping to correct them.
Native pronunciation, side by side.Learners record themselves and compare against a native speaker to hear exactly where pronunciation drifts.

Building voice-first AI for your platform?

30 minutes. Bring your data and your AWS setup. Walk out with a working architecture you can build to.

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Our process

Four stages from brief to live platform.

Conceptualization

Design

Development

Deployment

Fixed-scope build. Typical engagement: 8–12 weeks from MVP validation to scalable production deploy.

Our partner

You know the language. Now actually speak it.

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What's inside

Voice-first AI on a stack you can scale.

OpenAI and ElevenLabs through your own AWS infrastructure. Sub-second turn-around between learner voice in and teacher voice out.

Figure 1. Teacher AI voice-first pipeline: ElevenLabs scribe_v1 STT with Azure Speech fallback, OpenAI GPT-5-mini analysis, GPT-4o-mini + Google Cloud Translate, Kuroshiro / jieba for CJK tokenisation, ElevenLabs eleven_v3 TTS out.

The Results