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ESP32-S3 AI Voice Assistant: A Cloud-Augmented Embedded System for Real-Time Voice Interaction

Author(s) Neha Agrawal, Puja Gupta, Shaivi Barwe, Chandra Parakash Singar, Deepesh Agrawal
Country India
Abstract The rapid advancement of embedded systems and cloud-based artificial intelligence has opened new avenues for deploying intelligent, voice-activated interfaces on resource-constrained microcontrollers. This paper presents an ESP32-S3-based AI Voice Assistant that integrates real-time speech recognition, natural language understanding, and voice synthesis into a compact, standalone device costing under INR 1500. The system implements a five-stage pipeline: wake detection via button trigger, audio capture using an INMP441 I2S PDM microphone at 16 kHz/16-bit, cloud-based speech-to-text transcription via the Deepgram Nova-2 API, intelligent response generation using the Grok large language model (xAI), and high-quality text-to-speech synthesis delivered through a MAX98357A I2S Class-D amplifier. A deterministic six-state finite state machine governs pipeline transitions, ensuring predictable behaviour and graceful error recovery. PSRAM-aware dynamic memory allocation enables handling of audio buffers up to 512 KB. All twelve functional test cases passed, with end-to-end latency of 6-8 seconds on a 20 Mbps WiFi connection. The project demonstrates that ultra-low-cost embedded devices can serve as capable endpoints for cloud-augmented AI voice interaction, with potential applications in smart home control, educational technology, and accessible interfaces for differently-abled users.
Keywords ESP32-S3, Voice Assistant, Speech-to-Text, Text-to-Speech, Large Language Model, Embedded AI, I2S Audio, State Machine, Deepgram, Grok LLM, Edge Computing, IoT
Field Engineering
Published In Volume 7, Issue 5, May 2026
Published On 2026-05-19
DOI https://doi.org/10.70528/IJLRP.v7.i5.2203
Short DOI https://doi.org/hb4xbm
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