Open AccessA Closed‐Form Solution of Linear Spectral Transformation for Robust Speech Recognition
Author(s) -
Kim Donghyun,
Yook Dongsuk
Publication year - 2009
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.09.0209.0012
Subject(s) - transformation (genetics) , computational complexity theory , computer science , speech recognition , function (biology) , algorithm , mathematics , mathematical optimization , biochemistry , chemistry , evolutionary biology , biology , gene
The maximum likelihood linear spectral transformation (ML‐LST) using a numerical iteration method has been previously proposed for robust speech recognition. The numerical iteration method is not appropriate for real‐time applications due to its computational complexity. In order to reduce the computational cost, the objective function of the ML‐LST is approximated and a closed‐form solution is proposed in this paper. It is shown experimentally that the proposed closed‐form solution for the ML‐LST can provide rapid speaker and environment adaptation for robust speech recognition.