| Paper: | SLP-L10.3 | ||
| Session: | Speaker Adaptation | ||
| Time: | Friday, May 19, 10:40 - 11:00 | ||
| Presentation: | Lecture | ||
| Topic: | Speech and Spoken Language Processing: Speaker adaptation and normalization (e.g., VTLN) | ||
| Title: | A Non-linear Speaker Adaptation Technique Using Kernel Ridge Regression | ||
| Authors: | George Saon, IBM, United States | ||
| Abstract: | We propose a non-linear model space transformation for speaker or environment adaptation based on weighted kernel ridge regression (KRR). The transformation is given by a generalized least squares linear regression in a kernel-induced feature space operating on Gaussian mixture model means and having as targets the adaptation frames. Using the ``kernel trick'', the solution to the optimization problem is obtained by solving a system of linear equations involving the Gram matrix of the input variables. We show that MLLR is a special case of KRR when a linear kernel is employed. Furthermore, we study an efficient low-rank approximation to the kernel matrix termed ``rectangle method'', where the regressors are chosen to be a small set of clustered adaptation frames. Experiments conducted on the EARS database (English conversational telephone speech) indicate that KRR with a Gaussian RBF kernel outperforms standard regression class-based MLLR. | ||
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