This work presents a broad study on the adaptation of neural network acoustic
models by means of learning hidden unit contributions (LHUC) -- a method that
linearly re-combines hidden units in a speaker- or environment-dependent manner
using small amounts of unsupervised adaptation data. We also extend LHUC to a
speaker adaptive training (SAT) framework that leads to a more adaptable DNN
acoustic model, working both in a speaker-dependent and a speaker-independent
manner, without the requirements to maintain auxiliary speaker-dependent
feature extractors or to introduce significant speaker-dependent changes to the
DNN structure. Through a series of experiments on four different speech
recognition benchmarks (TED talks, Switchboard, AMI meetings, and Aurora4)
comprising 270 test speakers, we show that LHUC in both its test-only and SAT
variants results in consistent word error rate reductions ranging from 5% to
23% relative depending on the task and the degree of mismatch between training
and test data. In addition, we have investigated the effect of the amount of
adaptation data per speaker, the quality of unsupervised adaptation targets,
the complementarity to other adaptation techniques, one-shot adaptation, and an
extension to adapting DNNs trained in a sequence discriminative manner.

通过学习隐藏单元贡献的调整方法（LHUC），这项工作介绍了神经网络声学模型的适应性研究，将其扩展到发言人自适应训练和多种语音识别基准，并证明在测试和 SAT 方案中，LHUC 均能在不同程度的训练和测试数据不匹配的情况下显著提高词错误率。