logo

FluentAI Chatbot for Mandarin Learners

Published on
AIChatbot

FluentAI Chatbot for Mandarin Learners

Github: https://github.com/xfactor-toml/fluentai_mandarin

1. Summary

The project aims to provide an interactive platform where users can practice and improve their Mandarin vocal skills through both voice-based and text-based communications. Leveraging advanced technologies such as speech recognition, AI-driven chatbots, and speech synthesis, the application offers a comprehensive solution for Mandarin language learners. Key features of the project include Voice-to-Text Conversion, Chatbot Integration, Speech Output, and Pronunciation Evaluation, which combined, deliver a robust learning tool for users.

2. Responsibility

I was responsible for designing, developing, and integrating various components of this project.

Key tasks included:

  1. Requirements Analysis: Understanding user requirements and translating them into technical specifications.
  2. Design & Architecture: Creating a scalable architecture that connects various APIs and ensures smooth data flow between components.
  3. API Integration: Implementing API calls to Deepgram for Voice-to-Text conversion and ElevenLabs for Text-to-Speech conversion. Integrating OpenAI's chatbot API to enable interactive conversations.
  4. Frontend Development: Crafting a user-friendly interface for both text-based and voice-based interactions, ensuring seamless user experience.
  5. Backend Development: Building a robust backend to handle data processing, interactions between APIs, and user data management.
  6. Testing & QA: Conducting rigorous testing to ensure each feature functions as intended and that the system overall is reliable and efficient.
  7. Deployment: Overseeing the deployment process, ensuring all components are functioning smoothly in the live environment.

3. Technical Overview

  1. Voice-to-Text Conversion:

    • Technology Used: Deepgram API
    • Functionality: Captures voice input from the user and converts it to text with high accuracy, enabling real-time language practice.

  2. Chatbot Integration:

    • Technology Used: OpenAI's Chatbot API
    • Functionality: Engages users in conversational practice, providing human-like responses to text-based or voice-based queries.

  3. Speech Output:

    • Technology Used: ElevenLabs API
    • Functionality: Converts the chatbot's text responses back into speech, allowing users to hear and practice correct pronunciation.

  4. Pronunciation Evaluation:

    • Functionality: The system provides feedback on the user's pronunciation to help them understand areas for improvement. This feature aims to assist users in accurately mastering Mandarin tones and phonetics.

  5. System Architecture:

    • Frontend: Developed using modern web technologies such as HTML, CSS, and JavaScript to ensure a responsive and interactive user experience.
    • Backend: Built using Node.js to handle server-side logic, API integrations, and data processing.
    • Database: Utilized MongoDB for efficient data storage and retrieval.

4. Challenge

  1. Accuracy in Voice-to-Text Conversion:

    • Challenge: Achieving high accuracy in converting user’s spoken Mandarin to text, especially given the tonal nature of the language.
    • Solution: Extensively testing and fine-tuning the Deepgram API settings to optimize performance for Mandarin input.

  2. Natural Conversational Flow:

    • Challenge: Ensuring that the chatbot can handle various conversational scenarios in a natural and intuitive manner.
    • Solution: Leveraging OpenAI’s advanced language models, coupled with custom training and fine-tuning, to handle diverse user inputs and provide coherent responses.

  3. Latency in Speech Output:

    • Challenge: Minimizing the delay in converting text responses back to speech, which is crucial for maintaining a natural conversation pace.
    • Solution: Implementing asynchronous processing and optimizing API call efficiency to reduce latency.

  4. Pronunciation Feedback Mechanism:

    • Challenge: Developing an accurate system for evaluating and providing meaningful feedback on user pronunciation.
    • Solution: Integrating advanced phonetic analysis tools and incorporating user feedback to continually refine the evaluation algorithms.

  5. User Interface Design:

    • Challenge: Creating an intuitive and welcoming user interface that accommodates both novice and advanced learners.
    • Solution: Conducting user research and usability testing to design a frontend that meets user needs while maintaining engagement and simplicity.