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Speaker Identification Using Tensor Decomposition of Acoustic Models

This paper explores speaker identification based on the speaker adaptation via multilinear decomposition of a speaker model.Tucker decomposition of the third order mean Tensor of training speaker yields three subspaces corresponding to each mode. The mean of the mixtures for speakers is expressed as a product of the mixture space and a weight matrix comprising of the other two spaces.We update only the mean of the mixtures for the adaptation stage . During testing, log likelihood is used to identify the scores for the test speakers.Experiments conducted on the TIMIT and NIST 2003 databases shows comparable performance with the baseline even in channel mismatch conditions of the development and enrollment dataset. Furthermore, using higher order tensors, we can easily adapt this problem to include noise and environment factors as well.