Discriminative linear-transform based adaptation using minimum verification error

This paper presents an investigation of the minimum verification error linear regression (MVELR) method for discriminative linear-transform based adaptation. The MVE criterion is employed to estimate a set of discriminative linear transformations which achieve the smallest empirical average loss with the given adaptation data. The MVELR directly minimizes the total detection errors, some of which are results of characteristic mismatch in the given adaptation data. In this study, segment-based phonetic detectors reflecting an important processing layer in speech event detection are initially trained via the conventional maximum likelihood (ML) method and then refined via the general MVE method using the original training data. Then, the initial MVE-trained detectors are adapted by two kinds of adaption techniques, MLLR and MVELR, respectively, with the given adaptation data for comparison. The experiments are performed on a supervised adaptation scenario and the effectiveness of the adapted detectors is evaluated based on the total detection error. Experimental results confirm the proposed MVELR method considerably reduces the total error rate over all categories of the detectors compared to the MLLR.