A posteriori and a priori transformations for speaker adaptation in large vocabulary speech recognition systems

Abstract

The speaker-dependent HMM-based recognizers gives lower word error rates in comparison with the corresponding speaker-independent recognizers. The aim of speaker adaptation techniques is to enhance the speakerindependent acoustic models to bring their recognition accuracy as close as possible to the one obtained with speaker-dependent models. In this paper, we propose a method using test and training data for acoustic model adaptation. This method operates in two steps. The first one performs an a priori adaptation using the transcribed training data of the closest training speakers to the test speaker. This adaptation is done with MAP procedure allowing reduced variances in the acoustic models. The second one performs an a posteriori adaptation using the MLLR procedure on the test data, allowing mapping of Gaussians means to match the test speaker’s acoustic space. This adaptation strategy was evaluated in a large vocabulary speech recognition task. Our method leads to a relative gain of 15% with respect to the baseline system and 10% with respect to the conventional MLLR adaptation.