Memory-Efficient Keyword Spotting*

Efficient processing of speech input is an important basic signal processing task. The first step of speech processing is typically Keyword Spotting (KWS), which wakes up a smart edge device upon detection of a specific keyword. This work addresses an extremely memory-efficient implementation of KWS for edge devices with limited computational and memory resources. The proposed method uses a novel approach based on the inverse Discrete Cosine Transform (DCT), which aims to significantly reduce memory consumption without compromising accuracy. It not only reduces memory usage but also has the potential to improve accuracy. Extensive experiments were conducted using standard KWS dataset, and the results show that the method leads to a significant reduction in memory usage while maintaining previous state-of-the-art accuracy levels. The accuracy variations remain minimal, indicating that the method provides an effective balance between reducing memory consumption and preserving model performance.