Eigen-voice based anchor modeling system for speaker identification using MLLR super-vector
Achintya Kumar Sarkar, Srinivasan Umesh · 2011
In this paper, we propose an anchor modeling scheme where instead of conventional “anchor” speakers, we use eigenvectors that span the Eigen-voice space. The computational advantage of conventional Anchor-modeling based speaker identification system comes from representing all speakers in a space spanned by a small number of anchor speakers instead of having separate speaker models. The conventional “anchor” speakers are usually chosen using data-driven clustering and the number of such speakers are also empirically determined. The use of proposed eigenvoice based anchors provide a more systematic way of spanning the speaker-space and in determining the optimal number of anchors. In our proposed method, the eigenvector space is built using the Maximum Likelihood Linear Regression (MLLR) super-vectors of non-target speakers. Further, the proposed method does not require calculation of the likelihood with respect to anchor speaker models to create the speakercharacterization vector as done in conventional anchor systems. Instead, speakers are characterized with respect to eigen-space by projecting the speaker’s MLLR-super vector onto the eigenvoice space. This makes the method computationally efficient. Experimental results show that the proposed method consistently performs better than conventional anchor modeling technique for different number of anchor speakers.