Voice-Controlled Smart Assistant: Computer Engineering Guide
1. Introduction
Overview of the project.
Objectives of the system.
Scope of the system.
2. Requirements Analysis
Functional Requirements:
· - User roles: Admin and End-user.
· - Key functionalities: Voice recognition, Natural language processing, Task execution, and Feedback mechanisms.
Non-Functional Requirements:
· - Real-time processing, High accuracy, Scalability, Privacy, and Security.
3. System Design
Architecture:
· - Client-Server Architecture or Edge computing.
· - Integration of Speech-to-Text (STT) and Text-to-Speech (TTS) systems.
Data Flow Diagrams (DFDs):
· - Level 0: System overview.
· - Level 1: Modules such as Voice Input, Command Processing, and Task Execution.
Database Design:
· - Tables: Users, Commands, Responses, Logs.
4. Technology Stack
Frontend: Mobile apps (Flutter or React Native), Web apps (React.js or Angular).
Backend: Python (Flask/Django), Node.js, or Java (Spring Boot).
AI/ML Frameworks: TensorFlow, PyTorch, or Hugging Face Transformers.
APIs and Libraries:
· - Speech Recognition: Google Speech API, Mozilla DeepSpeech, or PocketSphinx.
· - TTS: Google TTS, Amazon Polly, or pyttsx3.
Database: MySQL, PostgreSQL, or MongoDB.
Hosting: AWS, Azure, or Google Cloud.
5. Implementation
Frontend Development:
· - User-friendly interface for command inputs and system settings.
· - Real-time voice capture integration.
Backend Development:
· - API development for STT and TTS processing.
· - Command parsing using NLP techniques.
· - Integration with external APIs for task execution (e.g., smart devices, weather updates).
Database Development:
· - Schema for user data, command history, and system responses.
6. Security
Secure user authentication (e.g., OAuth 2.0).
Encryption for sensitive data (AES/RSA).
Anonymization of voice data to ensure privacy.
7. Testing
Unit Testing: Validate individual components.
Integration Testing: Ensure seamless module interactions.
System Testing: Test the application end-to-end.
AI Model Testing: Evaluate performance and accuracy of voice recognition and NLP models.
8. Deployment
CI/CD Pipeline setup using tools like Jenkins or GitHub Actions.
Hosting on cloud platforms.
Real-time monitoring and logging using tools like ELK Stack.
9. Maintenance and Updates
Regular updates to AI models for better performance.
Bug tracking and fixes using tools like JIRA.
Monitoring user feedback for continuous improvement.
10. Appendix
Glossary of terms.
References and resources.