Attention-based End-to-End Models for Small-Footprint Keyword Spotting
Table 3: Performance of adding convolutional layers in the GRU (CRNN) attention-based model with soft attention. FRR is at 1.0 false alarm (FA) per hour.
['[BOLD] Channel', '[BOLD] Layer', '[BOLD] Node', '[BOLD] FRR (%)', '[BOLD] Params (K)']
For the CRNN attention-based model, we used one layer CNN that has a C(20×5)1×2 filter. We achieve the lowest FRR of 1.02% at 1.0 FA/hour with 84.1K parameters. Another observation is that 16-channel models work better than 8-channel models. By increasing layers, the 8-2-64 model achieves a great gain over the 8-1-64 model. But we cannot observe extra benefit when increasing the layers with 16-channel models.