  Designing an efficient keyword spotting (KWS) system that delivers
exceptional performance on resource-constrained edge devices has long been a
subject of significant attention. Existing KWS search algorithms typically
follow a frame-synchronous approach, where search decisions are made repeatedly
at each frame despite the fact that most frames are keyword-irrelevant. In this
paper, we propose TDT-KWS, which leverages token-and-duration Transducers (TDT)
for KWS tasks. We also propose a novel KWS task-specific decoding algorithm for
Transducer-based models, which supports highly effective frame-asynchronous
keyword search in streaming speech scenarios. With evaluations conducted on
both the public Hey Snips and self-constructed LibriKWS-20 datasets, our
proposed KWS-decoding algorithm produces more accurate results than
conventional ASR decoding algorithms. Additionally, TDT-KWS achieves on-par or
better wake word detection performance than both RNN-T and traditional TDT-ASR
systems while achieving significant inference speed-up. Furthermore,
experiments show that TDT-KWS is more robust to noisy environments compared to
RNN-T KWS.
