Full covariance modelling and adaptation in sub-bands

With regard to the current interest in sub-band based modelling in the ASR community, this paper explores the gains in recognition performance and complexity reduction achieved by sub-band based full covariance modelling and speaker adaptation. With sub-band features, instead of a single large covariance matrix, it is now possible to have a set of smaller matrices making it practical to use Gaussian distributions employing full covariance matrices. This benefit is further demonstrated to give a significant complexity reduction in the implementation of speaker adaptation by maximum likelihood linear regression. The use of sub-band cepstra moreover presents the opportunity of capturing localised discriminative cues which contribute to increased recognition. In light of these gains, this paper explores the advantages of sub-band full covariance modelling and presents experimental evaluation on the WSJCAMO continuous speech database