  A key task for speech recognition systems is to reduce the mismatch between
the training and evaluation data that is often attributable to speaker
differences. To this end, speaker adaptation techniques play a vital role to
reduce the mismatch. Model-based speaker adaptation approaches often require
sufficient amounts of target speaker data to ensure robustness. When the amount
of speaker level data is limited, speaker adaptation is prone to overfitting
and poor generalization. To address the issue, this paper proposes a full
Bayesian learning based DNN speaker adaptation framework to model
speaker-dependent (SD) parameter uncertainty given limited speaker specific
adaptation data. This framework is investigated in three forms of model based
DNN adaptation techniques: Bayesian learning of hidden unit contributions
(BLHUC), Bayesian parameterized activation functions (BPAct), and Bayesian
hidden unit bias vectors (BHUB). In all three Bayesian adaptation methods,
deterministic SD parameters are replaced by latent variable posterior
distributions to be learned for each speaker, whose parameters are efficiently
estimated using a variational inference based approach. Experiments conducted
on 300-hour speed perturbed Switchboard corpus trained LF-MMI factored
TDNN/CNN-TDNN systems featuring i-vector speaker adaptation suggest the
proposed Bayesian adaptation approaches consistently outperform the adapted
systems using deterministic parameters on the NIST Hub5'00 and RT03 evaluation
sets in both unsupervised test time speaker adaptation and speaker adaptive
training. The efficacy of the proposed Bayesian adaptation techniques is
further demonstrated in a comparison against the state-of-the-art performance
obtained on the same task using the most recent hybrid and end-to-end systems
reported in the literature.
