Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation
TABLE X: WER (%) on various TED development and test sets from IWSLT12 and IWSLT13 evaluations.
['Model', 'dev2010', 'tst2010', 'tst2011', 'tst2013']
We compared LHUC and SAT-LHUC to SAT-fMLLR training using TED tst2010 We also compared both techniques, including a comparison in terms of the amount of data used to estimate each type of transform. fMLLR transforms estimated on 10s of unsupervised data result in an increase in WER compared with the SI-trained baseline (16.1% vs. 15.0%). When combined with LHUC or SAT-LHUC some of this deterioration was recovered In this section we summarise our results, applying LHUC and SAT-LHUC to TED, AMI, and Switchboard.