SAT-LHUC: Speaker adaptive training for learning hidden unit contributions
Paweł Świętojański, Steve Renais · 2016
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.