ISCA Archive Interspeech 2012
ISCA Archive Interspeech 2012

Noise compensation for subspace Gaussian mixture models

Liang Lu, K. K. Chin, Arnab Ghoshal, Steve Renals

Joint uncertainty decoding (JUD) is an effective model-based noise compensation technique for conventional Gaussian mixture model (GMM) based speech recognition systems. In this paper, we apply JUD to subspace Gaussian mixture model (SGMM) based acoustic models. The total number of Gaussians in the SGMM acoustic model is usually much larger than for conventional GMMs, which limits the application of approaches which explicitly compensate each Gaussian, such as vector Taylor series (VTS). However, by clustering the Gaussian components into a number of regression classes, JUD-based noise compensation can be successfully applied to SGMM systems. We evaluate the JUD/SGMM technique using the Aurora 4 corpus, and the experimental results indicated that it is more accurate than conventional GMM-based systems using either VTS or JUD noise compensation.

doi: 10.21437/Interspeech.2012-109

Cite as: Lu, L., Chin, K.K., Ghoshal, A., Renals, S. (2012) Noise compensation for subspace Gaussian mixture models. Proc. Interspeech 2012, 306-309, doi: 10.21437/Interspeech.2012-109

  author={Liang Lu and K. K. Chin and Arnab Ghoshal and Steve Renals},
  title={{Noise compensation for subspace Gaussian mixture models}},
  booktitle={Proc. Interspeech 2012},