Eigenvoice-based MAP adaptation within correlation subspace

Abstract

In recent years, the eigenvoice approach has proven to be an efficient method for rapid speaker adaptation, which directs the adaptation according to the analysis of full speaker vector space. In this article, we developed a new algorithm for eigenspace-based adaptation restricting eigenvoices in clustered subspaces, and maximum likelihood (ML) criterion was replaced with maximum aposteriori (MAP) criterion for better parameter estimation. Experiments show that even with one sentence adaptation data this algorithm would result in 6.45 % error ratio reduction relatively, which overcomes the instability of maximum likelihood linear regression (MLLR) with limited data and is much faster than traditional MAP method. This algorithm is not highly-dependent on subspace number of division, thus it proved to be a robust adaptation algorithm.