14thAnnual Conference of the International Speech Communication Association

Lyon, France
August 25-29, 2013

Noise Adaptive Training for Subspace Gaussian Mixture Models

Liang Lu, Arnab Ghoshal, Steve Renals

University of Edinburgh, UK

Noise adaptive training (NAT) is an effective approach to normalise environmental distortions when training a speech recogniser on noise-corrupted speech. This paper investigates the model-based NAT scheme using joint uncertainty decoding (JUD) for subspace Gaussian mixture models (SGMMs). A typical SGMM acoustic model has much larger number of surface Gaussian components, which makes it computationally infeasible to compensate each Gaussian explicitly. JUD tackles this problem by sharing the compensation parameters among the Gaussians and hence reduces the computational and memory demands. For noise adaptive training, JUD is reformulated into a generative model, which leads to an efficient expectation-maximisation (EM) based algorithm to update the SGMM acoustic model parameters. We evaluated the SGMMs with NAT on the Aurora 4 database, and obtained higher recognition accuracy compared to systems without adaptive training.

Full Paper

Bibliographic reference.  Lu, Liang / Ghoshal, Arnab / Renals, Steve (2013): "Noise adaptive training for subspace Gaussian mixture models", In INTERSPEECH-2013, 3492-3496.