Last year we introduced the Subspace Gaussian Mixture Model

(SGMM), and we demonstrated Word Error Rate improvements

on a fairly small-scale task. Here we describe an extension to the

SGMM, which we call the symmetric SGMM. It makes the model

fully symmetric between the “speech-state vectors” and “speaker

vectors” by making the mixture weights depend on the speaker as

well as the speech state. We had previously avoided this as it introduces

difficulties for efficient likelihood evaluation and parameter

estimation, but we have found a way to overcome those difficulties.

We find that the symmetric SGMM can give a very worthwhile

improvement over the previously described model. We will also

describe some larger-scale experiments with the SGMM, and report

on progress toward releasing open-source software that supports

SGMMs.

PDF file

In  ICASSP

Publisher  IEEE

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