Design of compact acoustic models through clustering of tied-covariance Gaussians

We propose a new approach for designing compact acoustic models particularly suited to large systems that combine multiple model sets to represent distinct acoustic conditions or languages. We show that Gaussians based on mixtures of inverse covariances (MIC) with shared parameters can be clustered using an efficient Lloyd algorithm. As a result, more compact acoustic models can be built by clustering Gaussians across tied mixtures. In addition, we show that the tied parameters of MIC models can be shared across acoustic models and languages, making it possible to build more efficient multi-model systems which take advantage of a common pool of clustered Gaussians.