Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation
TABLE VII: WERs for adapted sequence-trained models used in IWSLT evaluation. Note, the results are not directly comparable to those reported on TED in Table VI due different training data and feature pre-processing pipelines (see referenced papers for system details).
['Model', 'tst2011', 'tst2013']
WER, similar to the WER of our TED system (15.4%), which also for IWSLT utilised 100 hours of out-of-domain AMI data. The 0.3% difference could be explained by characteristics of the out-of-domain data used (tst2013 is characterised by a large proportion of non-native speakers which is also typical for AMI data, hence benefits more our baseline systems). When comparing both adaptation approaches operating in an unsupervised manner one can see that LHUC gives much bigger improvements in WER compared to speaker-dependent layer, 2.1% vs. 0.6% absolute (14% vs. 4% relative) on tst2013. For less mismatched data (tst2011) adaptation is less important and our system has a WER 0.8% absolute higher compared with the more sophisticated system.