Data–selective Transfer Learning for Multi–Domain Speech Recognition
Table 2: WER (%) with domain specific acoustic models using PLP features)
['Domain', 'RD', 'TV', 'CT', 'MT', 'TK', 'RS', 'Total']
Six different domain–dependent MLE models were trained from the 10 hours of training data for each domain (in all of the experiments PLP features were used, unless stated otherwise). Each of these models was then used to decode the complete test set. Instead, cross–domain scores (train and test are mismatched) result in considerable performance decreases everywhere.