LinguoSync

Education

Original Idea

Language Practice Buddy A mobile app with daily speaking prompts and quick feedback loops.

Product Requirements Document (PRD): LinguoSync

1. Executive Summary

LinguoSync is a high-performance mobile application designed to bridge the "fluency gap" for intermediate language learners. By providing daily, AI-generated speaking prompts and near-instant, high-fidelity feedback, the app creates a low-pressure environment for users to practice vocal production. Utilizing state-of-the-art 2026 technologies—including React Native 0.83, OpenAI Whisper Large-v3 Turbo, and Temporal-driven FastAPI backends—LinguoSync delivers a professional-grade coaching experience that fits into a user's daily routine.

2. Problem Statement

Language learners often reach a "plateau" where their reading and listening skills far outpace their speaking ability. Traditional apps focus on vocabulary and grammar through text, while live tutors are expensive and high-pressure. Learners lack a consistent, immediate, and private way to practice speaking, resulting in "speaking anxiety" and poor pronunciation.

3. Goals & Success Metrics

  • Goal 1: Increase user speaking confidence through daily practice.
  • Goal 2: Provide actionable linguistic feedback in under 2 seconds.
  • Goal 3: Drive long-term retention through gamified streak mechanics.

Success Metrics

  • Daily Active Users (DAU): Target 15% WoW growth.
  • Session Completion Rate: >80% of users who start a prompt should finish the feedback loop.
  • Retention (D30): Target >40% retention for users who complete the 7-day onboarding streak.
  • Latency: Average "Time to Feedback" (TTF) < 1.5 seconds.

4. User Personas

  1. Elena (The Expat): A marketing professional who moved to Berlin. She can read menus but freezes when speaking to colleagues. She needs "survival" situational prompts.
  2. Kenji (The Business Pro): Uses English for international calls. He needs to master specific industry terminology and reduce his accent for better clarity.
  3. Maya (The Casual Learner): Learning Japanese for fun. She loves streaks and visual progress charts but has only 5-10 minutes a day to practice.

5. User Stories

  • As Elena, I want to practice ordering coffee so that I don't feel embarrassed in the morning.
  • As Kenji, I want to see a visual waveform of my speech compared to a native speaker so that I can identify exactly where my intonation fails.
  • As Maya, I want to receive a push notification with a fun prompt so that I don't lose my 50-day streak.

6. Functional Requirements

  1. AI Prompt Engine: Generates daily tasks based on proficiency level (e.g., "Describe your favorite childhood toy").
  2. High-Fi Audio Recorder: Records 48kHz WAV files with real-time waveform visualization.
  3. Instant Feedback Loop:
    • Speech-to-text transcription.
    • Pronunciation scoring (phoneme level).
    • Grammar and syntax correction with "Better way to say it" suggestions.
  4. A/B Playback: Toggle between user recording and native-speaker AI reference.
  5. Progress Dashboard: Longitudinal tracking of "Pronunciation Accuracy" and "Fluency Score."

7. Technical Requirements

Frontend (Mobile)

  • Framework: React Native 0.83.x (New Architecture: Fabric/TurboModules enabled).
  • SDK: Expo SDK 55.
  • Audio Recording: react-native-nitro-sound (for PCM/WAV performance).
  • Visualization: react-native-skia (GPU-accelerated waveforms at 120 FPS).
  • Auth: Auth0 SDK v5.x (Passkey-first authentication).

Backend

  • Framework: Python FastAPI (using AnyIO for async standard).
  • Orchestration: Temporal (Durable execution for AI transcription pipelines).
  • Inference: OpenAI Whisper Large-v3 Turbo (Transcription) and Google Cloud Chirp 3 (Dialect-specific assessment).
  • Database: PostgreSQL 17 (Declarative Range Partitioning by user/time).

Infrastructure

  • Storage: AWS S3 Express One Zone (for low-latency audio ingestion).
  • Compute: AWS Lambda (Provisioned Concurrency) with SnapStart enabled.
  • Real-time: AWS AppSync (GraphQL Subscriptions) for pushing feedback to the UI.

8. Data Model

| Entity | Attributes | Relationships | | :--- | :--- | :--- | | User | userId (UUID), email, proficiencyLevel, targetLang, passkeyId | 1:N with PracticeSession | | Prompt | promptId, text, difficulty, category, audioRefUrl | 1:N with PracticeSession | | PracticeSession | sessionId, userId, promptId, audioUrl, status (Pending/Complete) | 1:1 with FeedbackReport | | FeedbackReport | reportId, sessionId, transcription, pronunciationScore, grammarJSON | Linked to Session |

Note: Session logs use PostgreSQL 17 Partitioning by created_at for performance.

9. API Specification

POST /v1/sessions/upload

  • Purpose: Initial receipt of audio chunk/file.
  • Request: Multipart/form-data (audio file + promptId).
  • Response: 202 Accepted 
    { "jobId": "abc-123", "statusUrl": "/v1/jobs/abc-123" }

GET /v1/jobs/{jobId}

  • Purpose: Poll for AI report status (if WebSocket fails).
  • Response: 200 OK 
    { "status": "completed", "payload": { "score": 88, "corrections": "..." } }

10. UI/UX Requirements

  • Waveform Interaction: Users can scrub through the react-native-skia waveform to replay specific syllables.
  • Haptic Feedback: expo-haptics triggers on every 0.5s of recording and upon receiving feedback.
  • Visual Contrast: High-contrast "Correction Mode" highlighting grammar errors in red and improvements in green.

11. Non-Functional Requirements

  • Latency: S3 Express + Lambda SnapStart must ensure <500ms audio availability for the inference engine.
  • Security:
    • GDPR 2026: Mandatory "Purge-level" erasure for biometric voice data.
    • ISO/IEC 39794: Metadata standards for biometric interchange.
  • Accessibility: Full Screen Reader support for transcriptions; high-contrast modes for all feedback UI.

12. Out of Scope

  • Live 1-on-1 human tutoring (Future MVP).
  • Video recording/analysis.
  • Offline AI processing (Requires too much mobile compute).

13. Risks & Mitigations

  • Risk: AI "Hallucinations" in grammar feedback.
    • Mitigation: Use a multi-model verification step (GPT-5 checks Whisper's transcript against the prompt context).
  • Risk: High API costs for Whisper/Google.
    • Mitigation: Implement VAD (Voice Activity Detection) to skip processing silent audio files.

14. Implementation Tasks

Phase 1: Project Setup

  • [ ] Initialize project with React Native 0.83.x and Expo SDK 55
  • [ ] Configure Auth0 SDK v5.x with Passkey support
  • [ ] Set up FastAPI boilerplate with uv package manager
  • [ ] Configure PostgreSQL 17 with time-based partitioning for PracticeSession

Phase 2: Audio Foundation

  • [ ] Integrate react-native-nitro-sound for 48kHz recording
  • [ ] Build Skia-based waveform component for real-time visualization
  • [ ] Implement S3 Express One Zone upload utility with pre-signed URLs
  • [ ] Configure AWS Lambda SnapStart for the upload trigger

Phase 3: AI Pipeline

  • [ ] Set up Temporal worker for the transcription-to-feedback workflow
  • [ ] Integrate OpenAI Whisper Large-v3 Turbo via Bedrock
  • [ ] Implement Google Cloud Chirp 3 for phoneme-level scoring
  • [ ] Create logic for "Better way to say it" using GPT-5/Claude 3.5

Phase 4: UI & Gamification

  • [ ] Build the "Daily Prompt" card UI with react-native-reanimated
  • [ ] Implement the A/B comparison audio player
  • [ ] Build the 7-day streak tracker and dashboard
  • [ ] Integrate expo-haptics for tactile feedback on recording

Phase 5: Compliance & Launch

  • [ ] Implement ISO/IEC 39794 metadata logging for audio files
  • [ ] Create "Purge-level" data deletion logic for GDPR 2026 compliance
  • [ ] Perform latency stress test (Target: <1.5s TTF)
  • [ ] Submit to App Store/Play Store