Coding for STT to TTS
| Assignee |  Jhhspace |
|---|---|
| Status | In progress |
| Summary | This document outlines the tasks and prerequisites for coding a Speech-to-Text (STT) to Text-to-Speech (TTS) system. The tasks range from basic integration of STT and simple TTS responses to more advanced features like interactive learning with users, data analysis and pattern recognition, collaboration and feedback integration, and implementing a continuous improvement framework. |
 Project |  AI Catgirl Companion |
| Priority | Medium |
 Tags | CodingResearchSTTTTS |
Pre-requisite:
- Speech Recognition Basics:
- Basic understanding of speech recognition technologies.
Basic:
Voice and Facial Interaction Basics:
- Language: Python (using OpenCV and dlib for facial recognition).
- Tasks:
- Integrate a basic Speech-to-Text (STT) system to interpret spoken commands.
- Implement simple responses using Text-to-Speech (TTS) to communicate with users.
Intermediate:
- Interactive Learning with Users:
- Language: JavaScript (for web interactivity) or Python (for backend).
- Tasks:
- Enhance interactive features with voice commands using STT.
- Improve responses with more expressive TTS.
- Data Analysis and Pattern Recognition:
- Language: Python (using machine learning libraries like scikit-learn or TensorFlow).
- Tasks:
- Integrate voice data analysis alongside facial expression data.
- Combine facial and voice data for a more holistic understanding of user emotions.
Hard:
- Collaboration and Feedback Integration:
- Language: Python (for backend) and JavaScript (for frontend).
- Tasks:
- Enable users to provide feedback through spoken commands.
- Implement TTS for personalized feedback messages.
- Continuous Improvement Framework:
- Language: Python (for backend) and possibly other languages for specific tasks.
- Tasks:
- Incorporate voice data into the continuous learning framework.
- Schedule TTS updates to improve Coco's spoken responses over time.
