Data-driven UBM Generation via Tied Gaussians for GMM-Supervector Based Accent Identification

摘要

This paper presents a new approach to exploit data-driven universal background model (UBM) generation using tied Gaussians for accent identification (AID). The motivation of the proposed algorithm is to potentially utilize broad phonetic-specific accent characteristics by Gaussian mixture model (GMM) and examine data-driven phonetically-inspired UBM creation for GMM-supervector based accent classification. In this work, we discuss the issues involved in applying cumulative posterior probability based Gaussian selection and tree structure based UBM parameter estimation. Derivation and validation of the UBM refined by tied Gaussians are reported in this paper. Performance evaluations comparing our system with other well-known techniques for AID are also provided. Better performance is further achieved by fusing these acoustic-based accent classifiers. Comparison experiments conducted on the CSLU foreign-accented English (FAE) dataset show the effectiveness of the proposed method.