Attention-based End-to-End Models for Small-Footprint Keyword Spotting
Table 2: Performance of different encoder architectures with soft attention. FRR is at 1.0 false alarm (FA) per hour.
['[BOLD] Recurrent Unit', '[BOLD] Layer', '[BOLD] Node', '[BOLD] FRR (%)', '[BOLD] Params (K)']
We further explored the impact of encoder architectures with soft attention. Observing the LSTM models, the 1-128 LSTM model achieves the best performance with an FRR of 2.99% at 1.0 FA/hour. In Fig. This means making the LSTM network wider or deeper can achieve the same effect. However, observing Fig. The 1-128 GRU model presents a significant advantage over the 3-64 GRU model. In other words, increasing the number of nodes may be more effective than increasing the number of layers. Finally, the 1-128 GRU model achieves 1.49% FRR at 1.0 FA/hour.