基于自适应算法与多项式回归的抗噪语音识别技术.pdf




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ABSTRACT
Abstract
Prevailing speech recognition system could obtain a good recognition accuracy for
clean speech data, but their performance will degrade rapidly in noisy environments
owing to the mismatch between the acoustic models and the testing speech. There-
fore, noise robust speech recognition as a hot issue receives wide attention and has
been extensively studied .However, these researches can not fully meet the practical
requirement due to the complexity of the noisy environments. This thesis investigates
a novel and more efficient extension of GVP-HMM that can also model the trajectories
of feature space linear transforms. The transforms are trained under the constrained
maximum likelihood linear regression (CMLLR) criterion and would be applied on
features with auxiliary information to eliminate the mismatch from clean model and
test environment.
In this thesis, the theories of GVP-HMM and derivation of the featur