Evaluation of the combined use of MEMLIN and MLLR on the non-native adaptation task of hiwire project database

This paper describes the performance of the combination of Multi-Environment Model-based LInear Normalization, MEMLIN, which provides an estimation of the uncorrupted feature vector, with Maximum Likelihood Linear Regression, MLLR, for the collected database under the auspices of the IST-EU STREP project HIWIRE. In this work the results for the nonnative adaptation task (NNA) are presented. The HIWIRE project database consist on command and control aeronautics application utterances pronounced by non-native speakers which are digitally corrupted with airplane cockpit noise. Thus, three noise conditions are defined: low, medium and high noise. In the proposed system, each MEMLIN-normalized feature vector is decoded using the MLLR-adapted acoustic models. The experiments show that an important improvement is reached combining MEMLIN and MLLR methods for all kinds of non-native speakers and noise conditions.