Embedded Speech Recognition: Practice of Deploying Convolutional Neural Networks on Microcontrollers

This project demonstrates how to deploy a Convolutional Neural Network (CNN) on resource-constrained microcontrollers (MCUs) to achieve real-time digit speech recognition. The model is trained with 30,000 one-second audio samples, providing a practical example for edge AI applications. The core value lies in localized processing which reduces latency, protects privacy, and works without a network connection.

With the popularity of the Internet of Things (IoT), the trend of pushing AI from the cloud to the edge is emerging. Running ML models on MCUs can reduce latency, protect privacy, and work without a network. As a core of human-computer interaction, speech recognition localization is particularly important—for example, smart home control does not require data upload, ensuring privacy and reliability when the network is unstable.

Reasons for Choosing CNN: Local feature extraction adapts to the local correlation of speech spectra; parallel computing is efficient for MCUs; weight sharing reduces parameter count and saves memory. Feature Engineering: Raw audio is processed through framing, windowing, Fourier transform, Mel filtering, and logarithmic compression to obtain a Mel spectrogram (2D matrix). Network Architecture: Lightweight design: 2-3 convolutional layers (3x3/5x5 kernels + pooling), 1-2 fully connected layers, and an output layer with 10 neurons corresponding to digits 0-9. Training Strategy: Data augmentation (time stretching, pitch shifting, noise addition, etc.); regularization (Dropout, L2, early stopping) to prevent overfitting.

Model Quantization: Convert 32-bit floating-point numbers to 8-bit integers, reducing volume to 1/4 while maintaining over 95% accuracy (weight quantization or full quantization). Inference Optimization: Memory management (static allocation, buffer reuse, block processing); computation optimization (using DSP instructions, loop unrolling, lookup tables). Real-time Performance: Inference latency is controlled within 100ms; audio collection and inference are parallelized; result caching avoids repeated computation.

Audio Collection: The MCU collects microphone signals via ADC with a sampling rate of 8-16 kHz and bit depth of 12-16 bits, using double buffering for continuous collection. Processing Flow: Trigger collection → Record 1-second audio → Extract Mel spectrogram → CNN inference → Output result; the entire process is completed locally without a network.

Practical Scenarios: Voice dialing, password input, quantity control, device numbering, etc. Expansion Possibilities: Increase output neurons to expand vocabulary; collect more data; adjust network structure; optimize feature parameters; switch to binary classification for wake word detection.

Current Limitations: Vocabulary is only 10 digits; speaker-dependent; poor noise robustness; only supports isolated words. Improvement Directions: Optimization of keyword spotting; speaker adaptation; multilingual support; end-to-end learning to reduce manual feature engineering.

This project fully demonstrates the edge AI development process (data preparation → training → deployment optimization), proving that practical speech recognition can be implemented on MCUs. It provides developers with experience in designing networks under constraints and efficient deployment. With the development of TinyML, edge AI will become more prevalent, bringing intelligent experiences to the IoT.