Structured output layer with auxiliary targets for context-dependent acoustic modelling
Paweł Świętojański, Peter Bell, Steve J. Renals · 2015
In previous work we have introduced a multi-task training tech-nique for neural network acoustic modelling, in which context-dependent and context-independent targets are jointly learned. In this paper, we extend the approach by structuring the out-put layer such that the context-dependent outputs are depen-dent on the context-independent outputs, thus using the context-independent predictions at run-time. We have also investigated the applicability of this idea to unsupervised speaker adapta-tion as an approach to overcome the data sparsity issues that comes to the fore when estimating systems with a large num-ber of context-dependent states, when data is limited. We have experimented with various amounts of training material (from 10 to 300 hours) and find the proposed techniques are particu-larly well suited to data-constrained conditions allowing to bet-ter utilise large context-dependent state-clustered trees. Exper-imental results are reported for large vocabulary speech recog-nition using the Switchboard and TED corpora. Index Terms: multitask learning, structured output layer, adap-tation, deep neural networks