Sub-Model Short-Term Memory Convolutions for Keyword Spotting Systems on Device
Keyword Spotting (KWS) is becoming increasingly important as voice-controlled devices grow more widespread. While voice interaction with smartphones and smart TVs is already common, deploying KWS on heavily resource-constrained edge devices such as wearables remains challenging. These systems must meet high accuracy requirements while operating under strict constraints on computational power, memory footprint, and real-time latency. In this work, we present an application of the STMC (Short-Term Memory Convolutions) framework to adapt a modular CNN model for online, LSTM-like inference. Our approach reduces power consumption and redundant computations while maintaining the stability and s...
| Authors | Paweł Warlewski, Artur Czeczko, Artur Szumaczuk et al. |
|---|---|
| Primary category | cs.SD |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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