ESP32 Offline Voice Recognition
Offline voice recognition on the ESP32 enables devices to understand spoken commands without an internet connection. This is critical for low-latency response, privacy-sensitive applications, and battery-powered or remote systems.
Typical use cases include smart switches, robotics, industrial controls, toys, and assistive devices. This guide focuses on keyword spotting (KWS) and command recognition, which are the only practical forms of offline voice recognition on ESP32-class microcontrollers.
1. Understanding ESP32 Constraints
Hardware Limitations
- Dual-core Xtensa LX6 CPU up to 240 MHz
- ~520 KB shared SRAM
- 4–16 MB external flash (typical)
- No hardware floating-point unit
These constraints mean full speech-to-text is not feasible. ESP32-based systems are limited to small vocabularies (usually 5–50 commands) using highly optimized models.
2. Voice Recognition Approaches
Keyword Spotting (KWS)
Keyword spotting detects predefined words or phrases such as “Hey Device” or “Turn on light”.
- Low memory usage
- Fast and reliable
- Always-on capable
Command Classification
Command classification selects one command from a known set (e.g., start, stop, left, right). It is often triggered after a wake word.
3. Audio Capture Fundamentals
Microphone Selection
I2S MEMS microphones are strongly recommended for ESP32 voice projects.
- INMP441
- SPH0645
- ICS-43434
Analog microphones are discouraged unless paired with high-quality external ADC and filtering.
Audio Configuration
- Sample rate: 16 kHz
- Bit depth: 16-bit PCM
- Channels: Mono
4. Audio Preprocessing Pipeline
Accurate voice recognition depends heavily on audio preprocessing.
- Audio framing (20–30 ms)
- Windowing (Hamming)
- FFT
- Feature extraction
MFCC Features
- Frame length: 25 ms
- Frame stride: 10 ms
- FFT size: 512
- MFCC count: 10–20
ESP32 implementations typically use fixed-point MFCCs for performance.
5. Machine Learning Models
| Model | Accuracy | Speed | Memory |
|---|---|---|---|
| DNN | Medium | Fast | Low |
| CNN | High | Medium | Medium |
| DS-CNN | Very High | Fast | Low |
Depthwise Separable CNNs (DS-CNN) are the industry standard for embedded keyword spotting.
6. ESP32 Voice Recognition Frameworks
ESP-SR (Espressif)
- Wake word detection
- Command recognition
- Fully offline
- Pre-trained models
Memory usage typically ranges from 300–600 KB RAM and 1–2 MB flash.
TensorFlow Lite for Microcontrollers
- Custom-trained models
- INT8 quantization
- Higher flexibility
7. Training a Custom Model
- 100–300 samples per keyword
- Multiple speakers
- Noise and silence samples
Target model size should remain under 250 KB, with inference RAM usage below 100 KB.
8. Firmware Architecture
- Audio capture task
- Feature extraction task
- Inference task
- Application logic task
Pin inference to a single core and avoid dynamic memory allocation for real-time stability.
9. Wake Word + Command Flow
- Always-on wake word detection
- Switch to command recognition
- Timeout and return to wake mode
10. Power Optimization
- Disable Wi-Fi and Bluetooth
- Lower CPU frequency
- Use light sleep
- Optimize audio frame rate
11. Debugging and Testing
- Log confidence scores
- Monitor audio energy levels
- Test with background noise
12. Security and Privacy
Offline voice recognition ensures no audio data is transmitted or stored externally, improving privacy and predictability.
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