Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation

Tue, 06/28/2016 - 10:11 — pswietojanski

Title	Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation
Publication Type	Journal Article
Year of Publication	2016
Authors	Swietojanski, P, Li, J, Renals, S
Journal	IEEE/ACM Transactions on Audio, Speech, and Language Processing
Volume	24
Pagination	1450-1463
Date Published	Aug
ISSN	2329-9290
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
DOI	10.1109/TASLP.2016.2560534

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