Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation
TABLE VIII: WER(%) on AMI–IHM and AMI–SDM using adapted CNNs.
['Model', 'dev', 'eval']
Quality of data: We also investigated how the quality of the acoustic data itself affects the adaptation accuracies, keeping the other ASR components fixed. We performed an experiment on the AMI corpus using speech captured by individual headset microphones (IHM) and a single distant tabletop microphone (SDM). LHUC adaptation improves the accuracy in both experiments, although the gain for the SDM condition is smaller; however, the SDM system is characterised by twice as large WERs.