Efficient Polish-Language Keyword Spotting on Microcontrollers: Compact Neural Architectures, Quantization, and On-Device Validation on the Raspberry Pi Pico 2

Keyword spotting (KWS) is the always-on front end of voice interfaces; running it directly on microcontrollers, rather than streaming audio to the cloud, is essential for low-latency, privacy-preserving, and energy-efficient operation, yet it must reconcile high accuracy with severe memory, compute, and energy limits. Existing small-footprint KWS solutions are developed and benchmarked almost exclusively on English and are seldom validated on physical hardware or under quantization for other languages; their transferability to typologically different, consonant-rich languages such as Polish therefore remains largely unverified. We present a KWS pipeline for Polish, deployed and benchmarked on the RP2350 microcontroller (Raspberry Pi Pico 2, Raspberry Pi Ltd., Cambridge, Great Britain).We propose three compact architectures from the convolutional (CNN), convolutional recurrent (CRNN), and depthwise separable neural network (DS-CNN) families and benchmark them against the state-of-the-art BC-ResNet on a 25-keyword Polish vocabulary using MFCC features. All models are evaluated in full precision (Float32) and after eight-bit integer (INT8) post-training quantization, with inference latency measured directly on the target hardware. BC-ResNet attains the highest full-precision accuracy (97.81%) but is the most fragile under quantization, whereas the proposed CRNN is the most accurate quantized model (94.28%), the DS-CNN the smallest (46.15 KB), and the CNN the fastest (107.5 ms); all quantized models meet a one-second real-time budget. We further show that the accuracy ranking inverts after quantization, that memory savings are highly architecture-dependent, and that phonetically similar Polish words are the dominant source of error. These results offer practical guidance for deploying small-footprint KWS in Polish and other underrepresented languages.