  Discriminative training techniques define state-of-the-art performance for
automatic speech recognition systems. However, they are inherently prone to
overfitting, leading to poor generalization performance when using limited
training data. In order to address this issue, this paper presents a full
Bayesian framework to account for model uncertainty in sequence discriminative
training of factored TDNN acoustic models. Several Bayesian learning based TDNN
variant systems are proposed to model the uncertainty over weight parameters
and choices of hidden activation functions, or the hidden layer outputs.
Efficient variational inference approaches using a few as one single parameter
sample ensure their computational cost in both training and evaluation time
comparable to that of the baseline TDNN systems. Statistically significant word
error rate (WER) reductions of 0.4%-1.8% absolute (5%-11% relative) were
obtained over a state-of-the-art 900 hour speed perturbed Switchboard corpus
trained baseline LF-MMI factored TDNN system using multiple regularization
methods including F-smoothing, L2 norm penalty, natural gradient, model
averaging and dropout, in addition to i-Vector plus learning hidden unit
contribution (LHUC) based speaker adaptation and RNNLM rescoring. Consistent
performance improvements were also obtained on a 450 hour HKUST conversational
Mandarin telephone speech recognition task. On a third cross domain adaptation
task requiring rapidly porting a 1000 hour LibriSpeech data trained system to a
small DementiaBank elderly speech corpus, the proposed Bayesian TDNN LF-MMI
systems outperformed the baseline system using direct weight fine-tuning by up
to 2.5\% absolute WER reduction.
