/ / project

Intelligent Voice Bot

A voice-first conversational assistant that listens to user input, converts speech to text, augments prompts with prior context, and responds with spoken output.

Focus Voice AI • Conversational systems

Tools Speech recognition • Whisper • gTTS • LLM prompts

Repo View on GitHub

01 / problem

Making voice interaction feel natural

The goal of this project was to build a voice bot that can listen, understand, and respond in a conversational way. The challenge was not only converting speech to text, but also supplying the model with the right context so that responses felt relevant and coherent.

This led to a design where audio input, previous history, and LLM prompting all worked together in one loop.

02 / implementation

From audio input to spoken output

Converted input audio into text using speech recognition and Whisper-based transcription
Included prior conversation history in the request to support contextual replies
Generated responses using LLM prompting tuned for the assistant task
Converted output text back into audio using gTTS
Added audio classification logic to detect exit phrases and stop interaction cleanly

03 / system value

Useful conversational flow

What stands out in this project is the complete conversation cycle: capturing spoken input, understanding context, generating a response, and returning it in audio. That end-to-end flow is a strong example of applied voice AI design.

04 / takeaway

Voice + context + memory

This project shows how important context and interaction design are for voice systems. A bot becomes far more useful when it remembers the conversation, responds appropriately, and knows when to stop.

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