Voice AI is becoming more accessible, but language and accessibility still remain important challenges for many users in India.
As part of the Murf AI Voice for Bharat Challenge, I built SpeakEasy AI — a voice-based AI assistant designed to help users practice English through natural conversations.
🚀 What is SpeakEasy AI?
SpeakEasy AI is a conversational voice agent that allows users to speak naturally and receive AI-generated voice responses.
The idea is simple:
Speak naturally. Practice confidently. Improve one conversation at a time.
The agent is designed around everyday English practice and supports English, Hindi, and Hinglish interactions.
🛠️ Tech Stack
The project uses:
- Python
- LiveKit Agents
- Murf AI for voice generation
- LLM-based conversation
- Speech-to-text and text-to-speech
- FastAPI
- Next.js / frontend UI
- Git & GitHub
🎙️ Voice Interaction
The main goal was to make the interaction feel like a real conversation rather than a traditional chatbot.
Users can start a voice conversation, speak naturally, and receive spoken responses from the AI agent.
I also integrated a friendly female voice to make the experience more natural and approachable.
🧠 Multilingual Conversation
One of the important parts of the project is handling different ways users communicate.
For example, a user can switch between:
- English
- Hindi
- Hinglish
This makes the experience more comfortable for users who may not be completely confident speaking English.
🔄 Specialist Handoff
I also experimented with conversational routing.
For example, when a user asked for help with a mathematics problem, the main assistant could route the conversation toward a dedicated maths practice specialist.
This makes the architecture more flexible because different specialists can handle different tasks.
📊 Call Analytics
Another feature I implemented was a simple call analytics dashboard.
The dashboard tracks:
- Total calls
- Successful calls
- Failed calls
This provides a basic view of how the voice agent is performing.
💡 What I Learned
Building SpeakEasy AI helped me understand that a voice agent is much more than simply connecting speech-to-text with an LLM.
Important parts include:
- Natural conversation flow
- Voice quality and latency
- Multilingual interaction
- Agent routing
- Error handling
- Call analytics
- User experience
The biggest learning for me was understanding how different AI components work together to create a complete voice experience.
🇮🇳 Why Voice AI for Bharat?
India has a huge diversity of languages and communication styles.
Voice interfaces can make AI more accessible to people who may find traditional text-based interfaces difficult or less natural.
With better multilingual support and localized voice experiences, conversational AI can become much more useful for everyday learning and communication.
🚀 What's Next?
I plan to continue improving SpeakEasy AI by working on:
- Better multilingual conversations
- More specialist agents
- Improved conversation memory
- Better analytics
- More real-world use cases
This project has been a great hands-on experience in building production-oriented AI voice applications.
Thanks to the Murf AI Voice for Bharat Challenge for providing the opportunity to explore voice AI more deeply.
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