Attention-based End-to-End Models for Small-Footprint Keyword Spotting
Table 1: Performance comparison between Deep KWS and attention-based models with 2-64 network. FRR is at 1.0 false alarm (FA) per hour.
['[BOLD] Model', '[BOLD] FRR (%)', '[BOLD] Params (K)']
With similar size of parameters, the proposed attention models outperform the Deep KWS systems by a large margin. We also note that GRU is preferred over LSTM in both Deep KWS and the attention models. Not surprisingly, the soft attention-based model achieves the best performance. At 1.0 FA/hour, the GRU attention model reduces the FRR from 6.38% (GRU Deep KWS) down to 1.93% with a remarkable false rejection reduction.