In previous work we introduced a new missing data imputation method for ASR, dubbed sparse imputation. We showed that the method is capable of maintaining good recognition accurac...
This paper reports on an experiment to determine the optimal parameters for a speech recogniser that is part of a computer aided instruction system for assisting learners of Engli...
Huayang Xie, Peter Andreae, Mengjie Zhang, Paul Wa...
We introduce Bayesian sensing hidden Markov models (BS-HMMs) to represent speech data based on a set of state-dependent basis vectors. By incorporating the prior density of sensin...
In this paper, we present the Gauss-Newton method as a unified approach to optimizing non-linear noise compensation models, such as vector Taylor series (VTS), data-driven parall...
Mismatch between training and test conditions deteriorates the performance of speech recognizers. This paper investigates the combination of parametric histogram equalization (pHE...