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Speaker adaptation using generalised low rank approximations of training matrices

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2 Author(s)
Jeong, Y. ; Sch. of Electr. Eng., Pusan Nat. Univ., Busan, South Korea ; Kim, H.S.

A speaker adaptation method based on the low rank approximation of matrices (GLRAM) of training models is described. In the method, each model is represented as a matrix, and a set of such training matrices is decomposed into a set of speaker weights and two basis matrices for row and column spaces by reducing both row and column ranks of the training models. As a result, the speaker weight becomes a matrix, the row and column dimensions of which can be adjusted. In the isolated-word experiment, the proposed method showed better performance than both eigenvoice and MLLR for the adaptation data of about 20 s or longer.

Published in:

Electronics Letters  (Volume:46 ,  Issue: 10 )

Date of Publication:

May 13 2010

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