Discriminant local information distance preserving projection for text-independent speaker recognition

Abstract

A novel method is presented based on a statistical manifold for text-independent speaker recognition. After feature extraction, speaker recognition becomes a sequence classification problem. By discarding time information, the core task is the comparison of multiple sample sets. Each set is assumed to be governed by a probability density function (PDF). We estimate the PDFs and place the estimated statistical models on a statistical manifold. Fisher information distance is applied to compute distance between adjacent PDFs. Discriminant local preserving projection is used to push adjacent PDFs which belong to different classes apart to further improve the recognition accuracy. Experiments were carried out on the NIST SRE08 tel-tel database. Our presented method gave an excellent performance.