Learning Multiscale Features Directly From Waveforms
Table 3: Multiscale vs. single scale features. Holding the number of filters constant, single scale convolution with High frequency (1ms window, 1/4ms stride), Mid frequency (4ms, 1ms) or Low frequency (40ms, 10ms), are outperformed by a combination of all three (as diagrammed in Figure 1).
['# Features High (1ms)', '# Features Mid (4ms)', '# Features Low (40ms)', 'WER(%)']
It is clear that the High (2800Hz), Mid (1700Hz), and Low (940Hz) filter banks live up to their names, each learning filters that capture different frequency bands. When we jointly learn several scales, the filters exhibit a an heightened preference for representing different bands. Relaxing the requirement of each bank to cover the entire spectrum causes the High (3500Hz) banks to increase in mean frequency, and the Mid (1300Hz) and Low (480Hz) banks to decrease. Further, the WER is lowest for the multiscale features, despite the fact that it has fewer parameters and three times less large filters.