Differentiable Pooling for Unsupervised Acoustic Model Adaptation
TABLE III: WER(%) results for different subsets of adapted parameters of Diff-Lp model on TED (tst2010), AMI(eval) and SWBD (eval2000) test sets
['Model', '#SD Parameters', 'TED', 'AMI', 'SWBD']
Updating only p, rather than any other stand-alone pooling parameter, gives a lower WER than LHUC adaptation with the same number of parameters (cf Fig. Adapting the bias is more data-dependent with a substantial increase in WER for SWBD; this also significantly increases the number of adapted parameters. Hence we adapted either p alone, or p with LHUC in the remaining experiments