📘 Project Title AI Voice Assistant using Speech-to-Text, LLM API, and Text-to-Speech 🧩 Project Domain / Category

Artificial Intelligence / Natural Language Processing / Speech Processing / Web Application

📄 Abstract / Introduction

With the rapid advancement in Artificial Intelligence, voice-based systems have become an essential part of modern applications such as virtual assistants, customer support systems, and smart devices. These systems rely on a combination of Speech-to-Text (STT), Natural Language Processing (NLP), and Text-to-Speech (TTS) technologies to interact with users in a natural and efficient manner.

This project focuses on developing an AI-powered voice assistant that can listen to user input through speech, convert it into text, process the input using a Large Language Model (LLM) via API, and generate a meaningful response. The response is then converted back into speech to provide a complete conversational experience.

The proposed system eliminates the need for manual text input and demonstrates how modern AI services can be integrated to build intelligent, real-time applications. This project will help students understand API integration, speech processing, and modular system design.

🎯 Objectives To build a voice-based AI assistant To integrate Speech-to-Text for voice input processing To use an LLM API for generating intelligent responses To convert generated text into speech using TTS To develop a user-friendly interface for interaction ⚙️ Functional Requirements 🔹 1. Audio Input (Speech Capture) Capture user voice input using microphone Record audio in a suitable format (WAV recommended) 🔹 2. Speech-to-Text (STT) Convert recorded audio into text Ensure reasonable accuracy for short sentences Handle basic noise and pauses 🔹 3. LLM API Integration Send user text input to an LLM via API Receive and process the response Ensure proper API handling (authentication, request/response) 🔹 4. Text Processing Clean and format the text before sending to API Optionally include system prompts for better responses 🔹 5. Text-to-Speech (TTS) Convert AI-generated text into speech Play the response audio to the user Ensure clarity and proper pronunciation 🔹 6. Conversation Flow Maintain a loop for continuous interaction Allow user to exit using a command (e.g., “exit”, “stop”) 🔹 7. User Interface Provide a simple interface (CLI or Web-based) Show: User input (text) AI response (text) Audio playback 🔹 8. Error Handling Handle cases where: Audio is unclear API fails No input is detected 🧠 Non-Functional Requirements System should respond within a reasonable time (2–5 seconds) Code should be modular and well-structured API keys must be securely stored (e.g., environment variables) Application should be easy to run and test 🔬 Project Workflow Capture voice input Convert speech → text (STT) Send text → LLM API Receive response Convert response → speech (TTS) Output audio + display text 🧪 Tasks / Implementation Steps ✅ Phase 1: Research & Setup Understand STT, LLM APIs, and TTS Set up development environment ✅ Phase 2: Audio Processing Implement audio recording functionality Store and manage audio files ✅ Phase 3: STT Integration Convert audio input into text Test accuracy with different inputs ✅ Phase 4: LLM API Integration Register and obtain API key Send requests and handle responses Optimize prompts for better output ✅ Phase 5: TTS Integration Convert generated text into speech Ensure smooth playback ✅ Phase 6: System Integration Connect all modules (STT + API + TTS) Implement continuous interaction loop ✅ Phase 7: UI Development Develop a simple user interface Display input/output clearly ✅ Phase 8: Testing & Evaluation Test with multiple voice inputs Evaluate response accuracy and speed 📊 Evaluation Criteria Accuracy of speech recognition Quality of AI-generated responses Smoothness of speech output System response time Code structure and modularity User interface usability 📚 Prerequisites Basic knowledge of Python Understanding of APIs and HTTP requests Basic understanding of AI/NLP concepts Familiarity with audio processing (optional) 🛠️ Tools & Technologies Language: Python Environment: Jupyter Notebook / VS Code / PyCharm / Google Colab Libraries: Speech Processing (STT) API handling (requests/OpenAI SDK) Text-to-Speech (TTS) Audio processing (OpenCV optional for UI integration) 🚀 Optional Enhancements (Bonus) Add wake word detection (e.g., “Hey Assistant”) Implement real-time streaming (reduce delay) Add memory for conversation context Develop a web app using Streamlit or Flask Integrate with external services (e.g., weather API, WhatsApp bot)