Wake Word Detection with Alignment-Free Lattice-Free MMI
Table 4: Effect of alignment-free LF-MMI loss.
['FRR(%)', 'SNIPS (FAH=0.5)', 'Mobvoi (FAH=1.5)']
To compare our proposed alignment-free LF-MMI loss with regular LF-MMI and conventional cross-entropy loss for our task, we train a phoneme-based HMM-GMM system to generate the numerator lattice (for regular LF-MMI loss) or the forced alignments (for conventional cross-entropy loss) for the same sub-segmented and augmented training data. the network architectures are the same as what is used for alignment-free LF-MMI training except the final layer (depending on the loss being used). It is worth noting that we believe SNIPS results are more indicative of performance, as after manually listening to the false alarms in Mobvoi at this specific operating point, we found that all the false alarms (9 in total) are actually intentionally pronounced with a different tone on the last character “wen”, which is extremely difficult for the model to learn given the limited amount of data; some would even argue that those examples should be labeled as positive cases in order for the model to accommodate Chinese speakers with accents. The better performance of the system with alignment-free LF-MMI loss is possibly due to the capability of learning flexible alignments than GMM models.