Wake Word Detection with Alignment-Free Lattice-Free MMI
Table 3: Effect of data augmentation.
['FRR(%)', 'SNIPS (FAH=0.5)', 'Mobvoi (FAH=1.5)']
We augment the training data using the MUSAN corpus in the following way: we randomly apply additive noise from the “babble”, “music” and “noise” data sets separately on each copy of the original training data once. For each training example, “babble” is added as background noises 3 to 7 times with SNRs ranging from 13 to 20; “music” is added as background noises once with SNRs ranging from 5 to 15; “noise” is added as foreground noises at the interval of 1 second with SNRs ranging from 0 to 15. Then reverberation is also separately applied to the training data using the simulated RIRs with room sizes uniformly sampled from 1 meter to 30 meters. The above procedure increase the training data by a factor of 4. We then apply 3 way speed-perturbation on top of the original training set. The augmentation strategy together results in about 7× more training data. It can be seen the augmentation strategy is highly effective, where FRR with SNIPS is even 0 at FAH=0.5.