Speaker Identification with Edge AI
Identifying who is speaking from voice characteristics without recognizing what is said. Extracts speaker embeddings from short audio segments and matches against enrolled voice profiles stored on-device. Used for personalization, multi-user device access, and voice-based authentication. Privacy-preserving since raw audio never leaves the device. Requires more memory than keyword spotting due to embedding model complexity.
Hardware Requirements
| Minimum RAM | 128 KB |
| Minimum Flash | 1024 KB |
| Sensor Inputs | microphone |
| Typical Model Size | 100 KB (quantized int8) |
| Minimum Clock | 80 MHz |
Hardware Guides
No hardware guides for speaker identification yet. Use the MCU Checker to find compatible hardware.
Industry Applications
Orchestrate Speaker Identification with ForestHub
Your devices run speaker identification on-device. ForestHub on your Linux edge gateway ingests their results over MQTT/Modbus/OPC-UA, orchestrates the sense-reason-act loop as an auditable graph, and acts on the line — the LLM is one node among many.