End-to-end Anchored Speech Recognition
Table 3: Mask-based Model: with and without mask supervision.
['Model', 'Training Set', 'Test Set', 'WER', 'sub', 'ins', 'del', 'WERR(%)']
WERRs are all relative to the baseline model trained on device-directed-only data. For the Mask-based model without mask supervision, it achieves 3.9% WERR on the “hard” set while has a degradation of 34.8% on the “normal” set. On the other hand, with mask supervision (λ=0.1) corresponding to the multi-task training, it yields 12.6% WERR on the “hard” set while only 3.0% worse on the “normal” set. The performance gap between them can be attributed to the ability of mask prediction: while with mask supervision the recall is still around 70% (for frames labeled as “0”) and 98% (for frames labeled as “1”) on the held-out set, it is only 48% and 50% respectively without mask supervision.