A 7.5- μ W 35-Keyword End-to-End Keyword Spotting System With Random Augmented On-Chip Training

Fully integrated keyword spotting (KWS) systems designed for low-power operation face two major challenges. First, increasing the number of supported keywords significantly raises system complexity and power consumption. Second, most existing systems are not personalized to individual users, as they are trained on data from native English speakers, leading to degraded accuracy for non-native English speakers. In this article, we present a low-power KWS system that supports 35 keywords with a low-complexity design and enables personalized on-chip training to adapt to individual accents. The system is trained using only a single recording per keyword by applying random augmentation. A prototype implemented in 28-nm CMOS achieves an average accuracy of 91.2% on the accented speech command dataset (ASCD), which includes speech data from 20 speakers across nine different countries. The proposed system consumes5.31~μW during inference and7.49~μW during training.