Speaker adaptation by variable reference model subspace and application to large vocabulary speech recognition

Abstract

Recently, we presented a rapid speaker adaptation technique, reference model interpolation (RMI), which is based on the linear interpolation of speaker-dependent models and the a posteriori selection of reference models. The approach uses the a priori knowledge provided by a set of representative speakers to guide the estimation of a new speaker model in the speaker space. RMI achieved rapid supervised adaptation in phoneme decoding tasks. In this paper, we present two new results of RMI: firstly, we apply the RMI technique in a practical large vocabulary continuous speech recognition (LVCSR) system with unsupervised instantaneous adaptation. Secondly, we propose an evolutional subspace scenario which integrates the slow update of reference models with RMI rapid adaptation to achieve incremental adaptation. The unsupervised adaptation experiments carried out on broadcast news transcription task show encouraging results for both instantaneous and incremental adapatation.