Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Doi
Abstract
Speaker independent (SI) Tandem systems trained by joint optimisation
of bottleneck (BN) deep neural networks (DNNs) and
Gaussian mixture models (GMMs) have been found to produce
similar word error rates (WERs) to Hybrid DNN systems. A
key advantage of using GMMs is that existing speaker adaptation
methods, such as maximum likelihood linear regression
(MLLR), can be used which to account for diverse speaker
variations and improve system robustness. This paper investigates
speaker adaptation and adaptive training (SAT) schemes
for jointly optimised Tandem systems. Adaptation techniques
investigated include constrained MLLR (CMLLR) transforms
based on BN features for SAT as well as MLLR and parameterised
sigmoid functions for unsupervised test-time adaptation.
Experiments using English multi-genre broadcast (MGB3) data
show that CMLLR SAT yields a 4% relative WER reduction
over jointly trained Tandem and Hybrid SI systems, and further
reductions in WER are obtained by system combination