@conference {Swietojanski_ICASSP2016,
	title = {SAT-LHUC: Speaker Adaptive Training for Learning Hidden Unit Contributions},
	booktitle = {Proc. IEEE ICASSP},
	year = {2016},
	month = {March},
	address = {Shanghai, China},
	abstract = {This paper extends learning hidden unit contributions (LHUC) unsupervised speaker adaptation with speaker adaptive training (SAT). Contrary to other SAT approaches, the proposed technique does not require speaker-dependent features, the generation of auxiliary generative models to estimate or extract speaker-dependent information, or any changes to the speaker-independent model structure. SAT-LHUC is directly integrated into the objective and jointly learns speaker-independent and speaker-dependent representations. We demonstrate that the SAT-LHUC technique can match feature-space regression transforms for matched narrow-band data and outperform it on wide-band data when the runtime distribution differs significantly from training one. We have obtained 6.5\%, 10\% and 18.5\% relative word error rate reductions compared to speaker-independent models on Switchboard, AMI meetings and TED lectures, respectively. This corresponds to relative gains of 2\%, 4\% and 6\% compared with non-SAT LHUC adaptation. SAT-LHUC was also found to be complementary to SAT with feature-space maximum likelihood linear regression transforms.},
	url = {http://homepages.inf.ed.ac.uk/s1136550/data/Swietojanski_ICASSP2016.pdf},
	author = {Swietojanski, P. and Renals, S.}
}
