@article {7462247,
	title = {Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation},
	journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
	volume = {24},
	number = {8},
	year = {2016},
	month = {Aug},
	pages = {1450-1463},
	keywords = {acoustic signal processing, Acoustics, Adaptation, Adaptation models, AMI meetings, Aurora4, consistent word error rate reductions, deep neural networks (DNNs), DNN acoustic model, factorisation, feature extraction, Hidden Markov models, learning hidden unit contributions, learning hidden unit contributions (lHUC), LHUC, neural nets, neural network acoustic models, Neural networks, SAT, speaker adaptive training framework, speaker recognition, speaker-dependent manner, speaker-independent manner, speech recognition benchmarks, Switchboard, TED talks, Training, Transforms, unsupervised acoustic model adaptation, unsupervised learning},
	issn = {2329-9290},
	doi = {10.1109/TASLP.2016.2560534},
	author = {P. Swietojanski and J. Li and S. Renals}
}
