{"slug": "ava-intelligent-ai-voice-assistant-for-english-learning", "title": "Ava: Intelligent AI Voice Assistant for English Learning", "summary": "A developer built Ava, an AI-powered voice English learning assistant, using Murf Falcon, LiveKit, Deepgram, and Gemini. The system supports real-time conversations, persistent memory, live tools, outbound calls, human escalation, and specialist-agent handoffs. The project was developed over 10 days as part of the VoiceForBharat edition of the 10 Days of Voice Agents challenge.", "body_md": "Building Ava: A Real-Time AI Voice English Learning Assistant with Murf Falcon & LiveKit\n\n10 Days of Voice Agents — VoiceForBharat Edition\n\nOver the past 10 days, I built Ava, an AI-powered voice English learning assistant designed to help learners practice English through natural, real-time conversations.\n\nThe project started as a simple voice agent and gradually evolved into a complete voice AI system with memory, live tools, outbound calling, human escalation, call analytics, and specialist-agent handoffs.\n\nThe biggest goal was not just to make an AI that can talk, but to build an agent that can listen, understand, remember, take actions, and know when it needs help from a human or another specialist agent.\n\nWhat is Ava?\n\nAva is a conversational AI voice assistant designed primarily for English-learning practice.\n\nA learner can talk to Ava naturally instead of typing messages. Ava listens to the learner's voice, understands the conversation, generates a response, and speaks it back in real time.\n\nThe core voice pipeline is:\n\n🎤 User Voice → Deepgram STT → Gemini LLM → Murf Falcon TTS → 🔊 User\n\nThe real-time communication layer is handled using LiveKit.\n\n💡 Why Voice?\n\nLearning English is not only about reading and writing. Speaking and listening are equally important.\n\nA voice-based assistant makes it possible for learners to practice:\n\n🗣️ Speaking English\n\n👂 Listening comprehension\n\n💬 Natural conversations\n\n📚 Vocabulary\n\n✍️ Common mistakes\n\n🎯 Confidence while speaking\n\nInstead of interacting with a chatbot through text, learners can have a more natural conversation with Ava.\n\nDay 1 — Voice Agent Foundation 🎤\n\nI started by setting up the basic voice agent using LiveKit.\n\nThe initial goal was simple:\n\nListen to the user → process the speech → generate an answer → speak the answer.\n\nThis created the foundation for the rest of the project.\n\nDay 2 — Personality & Guardrails 🧠\n\nI gave Ava a clear personality and behavior.\n\nAva was designed to be:\n\nFriendly\n\nShort and conversational\n\nRespectful\n\nHelpful\n\nFocused on English learning\n\nI also added safety guardrails so Ava doesn't assist with things like hacking, fraud, password theft, OTPs, or other unsafe requests.\n\nDay 3 — Frontend Experience 💻\n\nI customized the frontend to make the voice-agent experience easier to understand.\n\nThe interface provides feedback about the current state of the voice conversation and connects the user with the AI agent.\n\nThis helped turn the project from a backend experiment into an actual user-facing application.\n\nDay 4 — Memory 🧠💾\n\nOne of the most interesting features was adding persistent memory.\n\nAva can remember useful information about returning users, such as:\n\nName\n\nLanguage preference\n\nCurrent learning level\n\nTopics covered\n\nCommon mistakes\n\nBut there is an important privacy rule:\n\nAva asks for permission before saving useful personal information.\n\nThis made the memory system more user-controlled instead of automatically storing everything.\n\nDay 5 — Live Tools 🌦️\n\nI added a live weather tool.\n\nFor example, a user can ask:\n\n\"What's the weather in Bhopal?\"\n\nInstead of guessing, Ava calls a weather API, retrieves current information, and explains the result naturally.\n\nThis was an important step because the agent could now take an action instead of only generating text.\n\nDay 6 — Outbound Voice Calls 📞\n\nNext, I extended the project to support outbound calling.\n\nThe agent could initiate a call and interact with a learner through a phone/SIP-based workflow.\n\nI also added rules for outbound conversations:\n\nIdentify itself as an AI\n\nExplain why it is calling\n\nAsk whether the learner has time\n\nRespect \"stop\" requests\n\nEnd the call if the learner is busy\n\nNever pressure the learner\n\nThis introduced another important lesson:\n\nVoice agents need to respect the user's time and consent.\n\nDay 7 — Human Escalation 👨🏫\n\nAva doesn't try to solve everything.\n\nIf a learner becomes confused, frustrated, or explicitly asks for a human teacher, Ava can offer to escalate the issue.\n\nBefore creating an escalation request, Ava asks for permission.\n\nThe request can contain information such as:\n\nUser name\n\nProblem\n\nWhat Ava already tried\n\nUrgency\n\nLanguage\n\nPreferred follow-up method\n\nA reference ID is then generated for the request.\n\nDay 8 — Call Analytics 📊\n\nI built a call analytics system to track conversations.\n\nThe dashboard can provide information such as:\n\nTotal calls\n\nSuccessful calls\n\nFailed calls\n\nPending calls\n\nSuccess rate\n\nCall duration\n\nThis helped me understand that building a voice agent isn't only about the conversation itself.\n\nObservability and analytics are also important parts of a real voice AI application.\n\nDay 9 — Specialist Agent Handoff 🔄\n\nI then added a specialist-agent architecture.\n\nAva acts as the main assistant.\n\nWhen a user asks something related to call analytics, Ava can transfer the conversation to a Call Analytics Specialist.\n\nFor example:\n\nUser: \"How many calls were successful?\"\n\nAva can respond:\n\n\"I'll connect you to our call analytics specialist.\"\n\nThe specialist then takes over the conversation.\n\nThis introduced the idea of multi-agent collaboration.\n\nDay 10 — Share & Reflect 🚀\n\nThe final day was about documenting the project, sharing what I learned, and making the project useful for other developers.\n\nThe biggest lesson from these 10 days was:\n\nA voice agent is much more than an LLM that can speak.\n\nIt needs speech recognition, reasoning, voice synthesis, real-time communication, memory, tools, safety, observability, and reliable handoffs.\n\n🏗️ Architecture\n\nThe overall system looks like this:\n\n```\n                ┌──────────────────┐\n                │      User        │\n                │  Voice / Browser │\n                └────────┬─────────┘\n                         │\n                         ▼\n                ┌──────────────────┐\n                │     LiveKit      │\n                │ Real-time Audio   │\n                └────────┬─────────┘\n                         │\n                         ▼\n                ┌──────────────────┐\n                │    Deepgram      │\n                │       STT        │\n                └────────┬─────────┘\n                         │\n                         ▼\n                ┌──────────────────┐\n                │   Gemini LLM     │\n                │ Reasoning / Chat │\n                └────────┬─────────┘\n                         │\n          ┌──────────────┼──────────────┐\n          ▼              ▼              ▼\n    ┌──────────┐   ┌──────────┐   ┌────────────┐\n    │ Memory   │   │  Tools   │   │ Specialist │\n    │ Database │   │ Weather  │   │   Agent    │\n    └──────────┘   └──────────┘   └────────────┘\n                         │\n                         ▼\n                ┌──────────────────┐\n                │    Murf Falcon   │\n                │       TTS        │\n                └────────┬─────────┘\n                         │\n                         ▼\n                🔊 Voice Response\n```\n\n⚙️ Tech Stack\n\nAI & Voice\n\nMurf Falcon — Text-to-Speech\n\nDeepgram — Speech-to-Text\n\nGoogle Gemini — LLM\n\nSilero VAD — Voice Activity Detection\n\nReal-Time\n\nLiveKit\n\nLiveKit Agents\n\nBackend\n\nPython\n\nSQLite\n\nHTTPX\n\nFlask\n\nFrontend\n\nNext.js\n\nReact\n\nTypeScript\n\n😅 Challenges I Faced\n\nThe project definitely did not work perfectly on the first attempt.\n\nI encountered several real-world problems during development:\n\n🔐 GitHub Secret Scanning\n\nGitHub blocked one of my pushes because an API key was detected inside a commit.\n\nI learned that simply changing the current file isn't enough if the secret still exists in Git history.\n\nThe solution was to remove the secret from the affected commits/history and keep only safe placeholders such as:\n\nGOOGLE_API_KEY=your_google_api_key\n\n🌐 LiveKit Connection Problems\n\nI also encountered LiveKit connection problems such as:\n\nRegionError(\"region fetch timed out\")\n\nand:\n\nsignal connection timed out\n\nAt one point, DNS resolution itself was failing.\n\nTesting with commands such as:\n\nnslookup murf-voice-agent-g91x9izl.livekit.cloud\n\ncurl -I [https://murf-voice-agent-g91x9izl.livekit.cloud](https://murf-voice-agent-g91x9izl.livekit.cloud)\n\nhelped identify whether the problem was in my application or the network connection.\n\n🎙️ Deepgram Connection Timeout\n\nAnother issue was:\n\nDeepgram did not receive audio data or a text message\n\nwithin the timeout window\n\nThis taught me that real-time voice systems are highly dependent on stable network connections and continuous audio flow.\n\n🔄 Voice Interruption & Latency\n\nI also worked through issues where Ava's voice would break or conversations would feel less smooth.\n\nThis required looking at the complete pipeline:\n\nMicrophone → LiveKit → STT → LLM → TTS → LiveKit → Speaker\n\nrather than assuming the problem was only in the TTS.\n\n🔐 Security\n\nOne of the most important lessons was never exposing API keys.\n\nKeys should stay inside environment variables:\n\n.env.local\n\nand should never be committed to GitHub.\n\nThe repository should contain only safe examples:\n\nGOOGLE_API_KEY=your_google_api_key\n\nMURF_API_KEY=your_murf_api_key\n\nDEEPGRAM_API_KEY=your_deepgram_api_key\n\nLIVEKIT_API_KEY=your_livekit_api_key\n\nLIVEKIT_API_SECRET=your_livekit_api_secret\n\n📂 GitHub Repository\n\n🔗 Source Code:\n\n[Add your GitHub repository link here]\n\nMake sure the public repository contains no API keys, phone numbers, caller information, database files, or other private data.\n\n🔮 What I Want to Build Next\n\nThere are several things I'd like to improve:\n\nBetter multilingual support\n\nMore natural conversation handling\n\nImproved interruption handling\n\nBetter voice latency\n\nMore learning-focused tools\n\nAdvanced learner analytics\n\nBetter specialist-agent routing\n\nProduction-grade monitoring\n\nMore robust phone-call infrastructure\n\n🎯 Final Takeaway\n\nThe biggest thing I learned during these 10 days is that building a voice agent is not just about connecting an LLM to a TTS model.\n\nA useful voice agent needs to:\n\nListen → Understand → Reason → Act → Speak → Remember → Escalate → Improve\n\nBuilding Ava gave me hands-on experience with the complete voice AI pipeline and taught me a lot about real-time systems, APIs, databases, AI tools, agent architecture, debugging, and production challenges.\n\nI'm proud to have completed:\n\n🚀 10 Days of Voice Agents — VoiceForBharat Edition\n\nBuilt with Murf Falcon + LiveKit + Gemini + Deepgram.\n\nA huge learning experience, and definitely just the beginning! 🎙️🤖", "url": "https://wpnews.pro/news/ava-intelligent-ai-voice-assistant-for-english-learning", "canonical_source": "https://dev.to/06divyam/ava-intelligent-ai-voice-assistant-for-english-learning-19bk", "published_at": "2026-08-15 18:01:05+00:00", "updated_at": "2026-08-15 18:12:20.651836+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-products", "ai-agents", "natural-language-processing", "developer-tools"], "entities": ["Ava", "Murf Falcon", "LiveKit", "Deepgram", "Gemini", "VoiceForBharat"], "alternates": {"html": "https://wpnews.pro/news/ava-intelligent-ai-voice-assistant-for-english-learning", "markdown": "https://wpnews.pro/news/ava-intelligent-ai-voice-assistant-for-english-learning.md", "text": "https://wpnews.pro/news/ava-intelligent-ai-voice-assistant-for-english-learning.txt", "jsonld": "https://wpnews.pro/news/ava-intelligent-ai-voice-assistant-for-english-learning.jsonld"}}