Subspace Gaussian mixture model: Difference between revisions

Revision as of 06:11, 5 October 2017

Revision as of 04:07, 13 September 2021

Line 1:

{{Multiple issues|

{{ref improveOrphan|date=September 20142021}}

{{more citations needed|date=September 2014}}

}}

'''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.<ref>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</ref>

Line 6 ⟶ 9:

{{reflist}}

{{computer science stub}}

[[Category:Speech recognition]]

{{computer science stub}}