Speech-to-Text Model Integration into Cross-Platform Speech App

This project seamlessly integrates a fine-tuned Whisper-based speech-to-text model with a cross-platform mobile application, designed to help users improve their speech, language skills, and communication abilities. The app provides real-time transcription of audio recordings, instant feedback, and interactive language practice, enabling users to refine their speech and track progress effectively.

The backend leverages OpenAI's Whisper model, fine-tuned with custom audio data, while the frontend, built using React Native, ensures the app is accessible on both Android and iOS. Firebase provides secure user authentication and data management.

• Role: Two developers (1 ML engineer for model fine-tuning and 1 app developer for app development and integration)
• Duration: 15 days (from ML model training to app deployment)
• Team Size: 2 developers

Key challenges included:

• Real-Time Speech Transcription and Feedback: Ensuring accurate, low-latency speech-to-text transcription for multiple languages.
• Cross-Platform Development: Integrating the ML model into a single codebase for Android and iOS using React Native.
• Multi-Language Support: Providing real-time transcription, text-to-speech, and translation in English, German, and French.
• Interactive Learning Tools: Designing features for live interview preparation, general speaking practice, and AI-guided exercises.
• Backend Efficiency: Handling large audio files, translations, and AI-generated feedback in real-time without lag.
• User Engagement: Creating a seamless, interactive, gamified, and personalized experience.

The team developed a cross-platform mobile app using React Native, integrating the fine-tuned Whisper model for real-time speech processing. Key solutions and features:

• Speech-to-Text & Text-to-Speech: Real-time audio transcription with immediate feedback and the ability to listen to correct pronunciation.
• Live Multi-Language Support: Users can speak in English, German, or French and receive instant transcription and translations.
• Live Translation: Transcribe and translate speech on-the-fly between supported languages.
• Interview Preparation Assistant: Practice interview questions live with scoring, AI-based feedback, and suggested improvement areas.
• General Speaking Assistant: Real-time conversational practice in multiple languages to improve fluency and confidence.
• Random Sentence Practice: Sentences generated by the backend for structured speaking practice.
• Contextual Grammar and Pronunciation Suggestions: AI-powered tips on sentence structure, grammar, and word pronunciation.
• Adaptive Difficulty Levels: Exercises adjust automatically based on user skill and progress.
• Sentiment & Tone Analysis: Evaluates speech delivery to help users improve presentation style and confidence.
• Progress Dashboard & Analytics: Personalized analytics showing accuracy, fluency trends, weak areas, and improvement over time.
• Gamified Experience: Achievements, streaks, and milestones to motivate consistent practice.
• Offline Mode (Planned Feature): Enable transcription and practice without an active internet connection.
• Integration with External Platforms (Planned): Export transcripts, practice results, and feedback reports for LinkedIn, resume prep, or learning management systems.

The integration of the Whisper model with the cross-platform app resulted in:

• Improved User Performance: Accuracy, fluency, and pronunciation scores improved consistently for both interview preparation and general speaking practice.
• High Engagement: Users returned regularly to practice, using live AI feedback, analytics, and gamified rewards.
• Cross-Platform Functionality: Smooth performance across Android and iOS devices.
• Rapid Development: Delivered the project in 15 days from ML fine-tuning to cross-platform deployment.
• High Retention: 85% user return rate within the first 2 weeks.
• Scalable Infrastructure: Backend optimized for real-time speech, translation, and AI feedback processing.
• Low Latency Feedback: Average response time for transcription, translation, and scoring under 1 second.
• User Satisfaction: 92% positive feedback on usability and overall experience.
• AI-Powered Insights: Users reported improved confidence in interviews and general speaking.

• Real-Time Transcription: Optimized the Whisper model for multilingual, low-latency transcription.
• Noisy Environment Accuracy: Trained the model with noise-robust audio data.
• Backend Load Handling: Used Retrofit and optimized server endpoints for smooth audio uploads, translations, and AI feedback.
• Cross-Platform Integration: Leveraged React Native for a single shared codebase across Android and iOS.
• Multi-Language Challenges: Implemented dynamic language selection and on-the-fly translation pipelines.

• Frontend: React Native, Firebase Authentication
• Backend: Python (Flask), OpenAI Whisper Model
• Libraries: Retrofit, OkHttp, Firebase SDK, TTS/Translation APIs
• Authentication: Firebase Authentication
• Tools: GitHub (CI/CD), Firebase (User Data)

Successfully integrated a fine-tuned ML model for real-time multilingual speech-to-text, text-to-speech, and live translation into a cross-platform app using React Native.

Designed and deployed an AI-powered, gamified, and adaptive learning experience with personalized feedback, analytics, and suggested improvements.

Built a scalable backend infrastructure capable of handling large audio streams, translations, and AI-generated recommendations in real-time.

Delivered a feature-rich application combining AI, language learning, and practical speech improvement tools.

The Speech App provides a comprehensive, AI-powered solution for improving communication skills. By supporting multilingual transcription, translation, interview practice, general speaking assistance, contextual grammar feedback, and advanced analytics, it empowers users to enhance fluency, pronunciation, confidence, and professional communication skills. Future enhancements may include offline support, additional languages, and integration with productivity tools.