A Feature Space Transformation Method for Personalization using Generalized I-Vector Clustering

摘要

we present a feature space transformation method for personalization. this method includes a generalization of i-vector based clustering that allows parameter tying of sub-loading matrices. this method trains i-vector parameters from the utterances of a mobile device; uncovering a low dimension space for clustering variability given the device. we show through empirical results impact of parameters of the generalized i-vector method. We conducted recognition experiments on an internal large vocabulary voice search system for gaming. The method achieved significant reductions of word error rates by 28%; compared to a per utterance adaptation system.