Unsupervised Adaptation with Domain Separation Networks for Robust Speech Recognition
Table 1: The WER (%) performance of unadapted acoustic model, GRL and DSN adapted DNN acoustic models for robust ASR on real and simulated development set of CHiME-3.
['System', 'Data', 'BUS', 'CAF', 'PED', 'STR', 'Avg.']
The clean DNN achieves 29.44% and 28.25% WERs on the real and simulated development data respectively. The GRL adapted acoustic model achieves 27.16% and 27.16% WERs on the real and simulated development data. The best WER performance for DSN adapted acoustic model are 24.15% and 23.82% on real and simulated development data, which achieve 11.08% and 12.30% relative improvement over the GRL baseline system and achieve 17.97 % and 17.69% relative improvement over the unadapted acoustic model. The best WERs are achieved when Nh=7 and α=8.0. By comparing the GRL and DSN performance at Nh=4, we observe that the introduction of private components and reconstructor lead to 5.1% relative improvements in WER.