Model dimensionality selection in bilinear transformation for feature space MLLR rapid speaker adaptation

Bilinear models based feature space Maximum Likelihood Linear Regression (FMLLR) speaker adaptation have showed good performance especially when the amount of adaptation data is limited. However, the model dimensionality selection is very critical to the performance of bilinear models and need more work to find the optimal selection method. In this paper, we present an empirical study on this issue and suggest using a piecewise log-linear function to describe the relationship between the relatively optimal dimensionality parameter and the variant amount of data. This relationship can be used to efficiently select the bilinear model dimensionality in FMLLR speaker adaptation with the variant amount of data for each test speaker to improve recognition performance on the English voice control dataset.