Local channel compensated method in Speaker Recognition System



Currently, i-vectors become the standard representation of speech context in speaker and language recognition method. Cosine Distance (CD) is the most popular scoring method. It uses Linear Discriminant Analysis (LDA) and Within Class Covariance Normalization (WCCN) to reduce the channel variabilities. The aim of this work is to reduce channel variabilities locally before applying the CD. The idea is to create a large i-vector graph from a training dataset. After clustering it with community detection algorithmsthe target and the test i-vectors are projected into this graph. Only their neighborhood are selected to train the LDA and WCCN. Results will be compared with the global channel compensated method.