Subspace Gaussian Mixture Models for speech recognition

We describe 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. We call this a Subspace Gaussian Mixture Model (SGMM). Globally shared parameters define the subspace. This style of acoustic model allows for a much more compact representation and gives better results than a conventional modeling approach, particularly with smaller amounts of training data.

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: Daniel Povey, Lukas Burget, Mohit Agarwal, Pinar Akyazi, Kai Feng, Arnab Ghoshal, Ondrej Glembek, Nagendra K. Goel, Martin Karafiát, Ariya Rastrow, Richard C. Rose, Petr Schwarz, Samuel Thomas
• Keywords: Gaussian Mixture Model, ICASSP 2010, Modeling Approach, Signal Processing, Subspace Gaussian Mixture