This is not a pair of headphones with an AI feature bolted on. It is an intelligent learning device built from the ground up — one that listens, understands, translates, and teaches. We designed the entire real-time interaction layer: from device-side speech recognition and cloud AI orchestration to cross-device protocols and content delivery, shaping a product that works as a portable AI learning coach.
Hardware product design · APP + cloud AI service architecture · Speech recognition, translation, TTS, pronunciation assessment integration · Content management system · User path restructuring · Third-party service evaluation and selection
The challenge wasn't AI — it was making AI work within the constraints of a physical device, a real network, and a real learner.
The scenario isn't a chatbot on a phone. It's a Bluetooth audio device that must coordinate APP, cloud AI services, and hardware playback — all while keeping latency low, cost manageable, and the learning experience unbroken.
The headset is a playback terminal and sensor — not a compute device. All AI capabilities (speech recognition, translation, TTS, example generation, assessment) run in the cloud, then route back through the APP to the hardware for playback. Designing this chain to feel instant is the real engineering.
Three layers must stay in sync: the APP handles connectivity and content acquisition, the cloud processes AI workloads, and the hardware manages Bluetooth audio streaming. A failure in any layer breaks the experience. We designed the full chain to degrade gracefully.
Real-time translation needs sub-200ms latency. Pronunciation assessment needs accuracy. TTS needs natural voice quality. Each capability has a different cost profile and service provider. We evaluated Azure, Alibaba, iFlytek, and Volcano Engine to find the right balance for each module.
The original flow required 5 steps: translate → tap word → add to vocabulary → wait for generation → start conversation practice. We restructured the path to collapse learning behaviors into word cards, eliminated generation wait times, and reduced friction to keep learners engaged.
We decomposed AI into discrete, composable modules — speech recognition, real-time translation, TTS playback, example sentence generation, shadowing practice, and pronunciation assessment — forming a complete learning loop around the cycle of recognize → understand → generate → feedback.
We identified that the original 5-step learning path created excessive friction and wait time. We restructured the core interaction to consolidate learning behaviors into word cards and vocabulary, shortened the operation steps, and reduced dependency on real-time generation — dramatically improving usability in the hardware-constrained scenario.
We evaluated speech recognition, translation, TTS, and assessment services across Azure, Alibaba Cloud, iFlytek, and Volcano Engine — making product-level priority decisions between real-time performance, API cost, and experience stability. We adopted a phased approach: run through with existing services first, optimize later.
Beyond the AI interaction layer, we designed a complete content management backend — supporting multi-language content, category hierarchies, album/episode structures, and playback interfaces. This separates AI-generated interactions from stable content consumption, ensuring the device always has something valuable to deliver.