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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 13, NO. 6, NOVEMBER 2005 1161 Noise Robust Speech Recognition Using Feature
 

Summary: IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 13, NO. 6, NOVEMBER 2005 1161
Noise Robust Speech Recognition Using Feature
Compensation Based on Polynomial
Regression of Utterance SNR
Xiaodong Cui, Student Member, IEEE, and Abeer Alwan, Senior Member, IEEE
Abstract--A feature compensation (FC) algorithm based on
polynomial regression of utterance signal-to-noise ratio (SNR) for
noise robust automatic speech recognition (ASR) is proposed. In
this algorithm, the bias between clean and noisy speech features
is approximated by a set of polynomials which are estimated
from adaptation data from the new environment by the expecta-
tion-maximization (EM) algorithm under the maximum likelihood
(ML) criterion. In ASR, the utterance SNR for the speech signal is
first estimated and noisy speech features are then compensated for
by regression polynomials. The compensated speech features are
decoded via acoustic HMMs trained with clean data. Comparative
experiments on the Aurora 2 (English) and the German part of
the Aurora 3 databases are performed between FC and maximum
likelihood linear regression (MLLR). With the Aurora 2 experi-
ments, there are two MLLR implementations: pooling adaptation

  

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

 

Collections: Computer Technologies and Information Sciences