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Speech Recognition Mobile Application for Children’s Reading Education
Client: Educational technology company
QA
Expertise:
Tech Stack:
DevOps
UX/UI
PM
AI/ML
Frontend
Optuna
Comet
MongoDB
Kubernetes
Keras
PyAudio
TensorFlow
PyTorch
AWS (S3, Sagemaker, Studio)
Docker
PostgreSQL
Kaldi
Silero-models
VOSK
Google Cloud
Scipy
Pandas
Numpy
Business Analyst
Backend
The client is an educational technology company dedicated to creating innovative learning tools for children. Their goal is to enhance reading proficiency by integrating cutting-edge speech recognition technology into their mobile application.
About the Client:
The project aimed to develop a Speech-to-Text (STT) solution tailored to teaching children how to read. The app required high-precision pronunciation analysis to provide accurate feedback and guide children in improving their reading skills. Given the unique requirements of monitoring children’s speech patterns, off-the-shelf solutions were unsuitable, necessitating the creation of custom algorithms and models.
About the Project

Challenges & Objectives

Challenges:
  1. Precision in Pronunciation Analysis: The app needed to recognize and evaluate children’s speech accurately to ensure effective learning.
  2. Data Limitations: Existing datasets lacked sufficient samples of children’s speech patterns for reliable training.
  3. Customization: Generic STT solutions could not meet the app’s specialized requirements for precision and feedback.
Objectives:
  1. Design a highly accurate STT model capable of recognizing children’s speech and providing constructive feedback.
  2. Develop custom algorithms to handle pronunciation analysis and ensure real-time performance.
  3. Integrate the model seamlessly into a mobile application with scalability and reliability.
Implementation
The development process followed an iterative approach to achieve optimal performance and user experience.
01
Data Collection and Augmentation:
  • Curated a dataset of children’s speech samples from multiple sources.
  • Augmented data with synthesized speech patterns to improve model robustness.
02
Custom Architecture Development:
  • Designed custom STT and TTS architectures using TensorFlow, PyTorch, and Hugging Face libraries.
  • Implemented speech pattern reconstruction techniques for enhanced pronunciation analysis.
03
Algorithm Integration:
  • Developed clusterization algorithms for segmenting and analyzing speech data.
  • Applied sound transfer techniques to ensure precise pattern recognition.
04
Model Training and Optimization:
  • Leveraged AWS (Sagemaker, Studio) for scalable model training and deployment.
  • Used Comet and Optuna for hyperparameter tuning and performance tracking.
05
Mobile App Integration:
  • Deployed the solution into a mobile app using Docker and Kubernetes for scalability.
  • Enabled real-time feedback for users with optimized Redis and Kafka pipelines.

Outcomes & Business Impact

  1. Enhanced Accuracy: Achieved a 92% accuracy rate in children’s speech recognition.
  2. Real-Time Feedback: Delivered instant and precise pronunciation feedback to children.
  3. Custom Fit: Designed a tailored solution that outperformed generic STT models in this niche use case.
  4. Scalable Deployment: The app handled thousands of concurrent users with no latency issues.
Business Impact:
  1. Increased Engagement: Improved learning outcomes led to a 40% increase in app usage.
  2. Cost Savings: Custom algorithms reduced the reliance on expensive third-party STT services, saving $200K annually.
  3. Market Leadership: Positioned the client as an innovator in educational technology with proprietary STT capabilities.
  • +40% User Engagement: Children spent more time practicing with real-time feedback.
  • +92% Accuracy: High precision in speech recognition enhanced learning outcomes.
  • $200K Annual Savings: Reduced operational costs through custom solutions.
  • Scalable Performance: Seamless app experience for thousands of users.
Results & ROI
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