Learning Multiscale Features Directly From Waveforms
Table 4: Decoupling time and frequency resolution. Window size shown in parentheses. Adding additional filters at the same stride (1/4ms, 1ms, 10ms) significantly improves performance. All models have an additional bottleneck layer (Fully Connected, 161) inserted after pooling to maintain the same number of features as previous experiments. ∗Increasing the bottleneck layer from 161 to 800 leads to a small improvement.
['# Features High (1ms)', '# Features Mid (4ms)', '# Features Low (40ms)', 'WER(%)']
We explore the value of increased resolution by performing the same multiscale experiments as the previous section, but increasing the number of filters. A fully-connected layer with output dimension of 161 is added above the concatenated feature maps in order to produce the same number of features as the previous experiments. Since using more filters at short strides is more costly in terms of both computation and memory, we specifically increase the number of filters at long strides. Further increasing the number of filters, and expanding the size of the bottle neck leads to smaller gains. showing an impressive 20.7% cumulative improvement in WER relative to the spectrogram baseline.