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Rapid Speaker Adaptation using Regression-Tree based Spectral Peak Alignment Shizhen Wang1
 

Summary: Rapid Speaker Adaptation using Regression-Tree based Spectral Peak Alignment
Shizhen Wang1
, Xiaodong Cui2
and Abeer Alwan1
1
Department of Electrical Engineering, UCLA, CA, 90095
2
IBM T. J. Watson Research Center, Yorktown Heights, NY 10598
szwang@icsl.ucla.edu, cuix@us.ibm.com and alwan@ee.ucla.edu
Abstract
In this paper, regression-tree based spectral peak alignment is
proposed for rapid speaker adaptation using the linearization of
VTLN. Two different regression classes are investigated: phonetic
classes (using combined knowledge and data-driven techniques)
and mixture classes. Compared to MLLR and VTLN, improved
performance can be obtained for both supervised and unsuper-
vised adaptations on both medium vocabulary and connected dig-
its recognition tasks. To further improve the performance, MLLR
was integrated into this regression-tree based peak alignment. Ex-
perimental results show that the performance improvements can be

  

Source: Alwan, Abeer - Electrical Engineering Department, University of California at Los Angeles

 

Collections: Computer Technologies and Information Sciences