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MLLR-LIKE SPEAKER ADAPTATION BASED ON LINEARIZATION OF VTLN WITH MFCC FEATURES
 

Summary: MLLR-LIKE SPEAKER ADAPTATION BASED ON LINEARIZATION OF
VTLN WITH MFCC FEATURES
Xiaodong Cui and Abeer Alwan
Department of Electrical Engineering
University of California, Los Angeles, CA 90095
Email: xdcui@icsl.ucla.edu, alwan@icsl.ucla.edu
Abstract
In this paper, an MLLR-like adaptation approach is proposed
whereby the transformation of the means is performed deter-
ministically based on linearization of VTLN. Biases and adap-
tation of the variances are estimated statistically by the EM al-
gorithm. In the discrete frequency domain, we show that un-
der certain approximations, frequency warping with Mel-últer-
bank-based MFCCs equals a linear transformation in the cep-
stral domain. Utilizing the deduced linear relationship, the
transformation matrix is generated by formant-like peak align-
ment. Experimental results using children's speech show im-
provements over traditional MLLR and VTLN. The improve-
ments occur even with limited amounts of adaptation data.
1. Introduction

  

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

 

Collections: Computer Technologies and Information Sciences