Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation
TABLE III: WER(%) for different sampling strategies and SAT-LHUC training (TED tst2013)
['Model', 'Baseline', 'WER (%) for sampling strategies Per Speaker', 'WER (%) for sampling strategies Per Segment', 'WER (%) for sampling strategies Per Frame']
Prior to SAT-LHUC training we need to decide on how and which data should be used to estimate speaker-dependent and speaker-independent transforms. For speaker- and segment-level transforms we decide which speakers or segments are going to be treated as SI or SD prior to training. For the frame-level SAT-LHUC approach, the SI/SD decisions are made separately for each data-point during training. In either scenario we ensure that the overall SD/SI ratio determined by γ parameter is satisfied. Speaker-level SAT-LHUC training provides the highest WERs for both SI and SD decodes. Segment-level and frame-level SAT-LHUC training result in similar WERs for SI decodes, with a small advantage (0.1% abs.) for the frame-level approach after adaptation.