How to Build an Interview Agent

In this article, we will guide you through building a voice-based interview agent. The agent is designed to interact with users in real-time, providing voice-based input and output, maintaining conversation awareness, and asking questions based on the flow of the discussion. Below, we will detail the required functionalities, technologies, and workflow to implement this solution.

Functionalities

1. Voice-Based Input and Output: Users interact with the agent through spoken language, and the agent responds vocally.
2. Conversation Awareness: The agent remembers the conversation context to maintain coherence in its responses.
3. Dynamic Question Flow: The agent asks relevant questions based on the ongoing conversation and adjusts its queries as per the user’s responses.

Technologies Used

• OpenAI LLM: To generate context-aware responses.
• gTTS (Google Text-to-Speech): To convert the agent’s textual responses into speech.
• SpeechRecognition Module: To convert user’s speech input into text.
• FastAPI: For creating the backend API.
• HTML Frontend: For the browser interface where users interact with the agent.

Workflow

The interview agent’s architecture is divided into frontend and backend components:

Frontend Workflow

1. Capture Audio Input:
   • Use the browser’s microphone to record the user’s voice input.
2. Send Audio to Backend:
   • Transmit the recorded audio to the backend for transcription and processing.

Backend Workflow

1. Audio Transcription:
   • Use the SpeechRecognition module to convert the audio input into text.
2. Contextual Response Generation:
   • Pass the transcribed text to the OpenAI LLM, which generates a response based on the conversation history and interview flow.
3. Text-to-Speech Conversion:
   • Use gTTS to convert the generated response text into an audio file.
4. Return Response to Frontend:
   • Send the audio file back to the frontend for playback.

Implementation Steps

1. Set Up the Frontend

• Use HTML and JavaScript to build a simple interface that:
  • Captures audio input from the user.
  • Displays the transcription of the user’s input.
  • Plays the audio response from the agent.

2. Implement the Backend

• Create a FastAPI application with endpoints for:
  • Receiving audio input.
  • Transcribing audio to text.
  • Generating a response using the OpenAI LLM.
  • Converting the response text to audio.
  • Sending the audio response back to the frontend.

3. Maintain Conversation Awareness

• Store conversation history in memory or use a database if persistence is required. Pass this history to the OpenAI LLM to maintain context.

4. Connect Frontend and Backend

• Use JavaScript to send audio input to the FastAPI backend and play the response audio.

Enhancements

• Conversation Flow Control: Design a flowchart of potential interview paths and integrate logic for the agent to navigate these paths based on user responses.
• Improved Speech Recognition: Use custom acoustic models for domain-specific terms.
• Deployment: Host the solution on cloud platforms like AWS, Azure, or Google Cloud for scalability.

By following these steps, you’ll have a functional interview agent capable of facilitating dynamic and engaging voice-based interactions. Happy coding!