Subspace mixture model for low-resource speech recognition in cross-lingual settings

The subspace Gaussian mixture model (SGMM) has been exploited for cross-lingual speech recognition. The general motivation is that the subspace parameters can be estimated on multiple source languages and then transferred to the target language. In this work, we investigate an extension to SGMM, referred to as subspace mixture model (SMM), in which subspace parameters on the target language are casted as a linear mixture of the subspaces derived from source languages. This approach reduces the number of SGMM model parameters, while retaining the flexibility of subspace learning on the target language. Experiments show that the proposed SMM method outperforms SGMM significantly when the target language has limited training data.