|Title||Learning Hidden Unit Contributions for Unsupervised Speaker Adaptation of Neural Network Acoustic Models|
|Publication Type||Conference Paper|
|Authors||Swietojanski, P, Renals, S|
|Conference Name||Proc. IEEE Workshop on Spoken Language Technology|
|Conference Location||Lake Tahoe, USA|
This paper proposes a simple yet effective model-based neural network speaker adaptation technique that learns speaker- speciﬁc hidden unit contributions given adaptation data, without requiring any form of speaker-adaptive training, or labelled adaptation data. An additional amplitude parameter is deﬁned for each hidden unit; the amplitude parameters are tied for each speaker, and are learned using unsupervised adaptation. We conducted experiments on the TED talks data, as used in the International Workshop on Spoken Language Translation (IWSLT) evaluations. Our results indicate that the approach can reduce word error rates on standard IWSLT test sets by about 8–15% relative compared to unadapted systems, with a further reduction of 4–6% relative when combined with feature-space maximum likelihood linear re- gression (fMLLR). The approach can be employed in most existing feed-forward neural network architectures, and we report results using various hidden unit activation functions: sigmoid, maxout, and rectifying linear units (ReLU).