Subspace Gaussian mixture model

From Wikipedia, the free encyclopedia

This is an old revision of this page, as edited by JoeNMLC (talk | contribs) at 16:08, 13 September 2021 (Successfully de-orphaned!♦ Wikiproject Orphanage: You can help!♦). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

Subspace Gaussian mixture model (SGMM) is an acoustic modeling approach in which all phonetic states share a common Gaussian mixture model structure, and the means and mixture weights vary in a subspace of the total parameter space.[1]

References

  1. ^ Povey, D : Burget, L. ; Agarwal, M. ; Akyazi, P. "Subspace Gaussian Mixture Models for speech recognition", IEEE, 2010, Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, pp. 4330–33, doi:10.1109/ICASSP.2010.5495662