1 research outputs found
A direct criterion minimization based fMLLR via gradient descend
Adaptation techniques are necessary in automatic speech recognizers
to improve a recognition accuracy. Linear Transformation methods (MLLR
or fMLLR) are the most favorite in the case of limited available data. The fMLLR
is the feature-space transformation. This is the advantage with contrast to
MLLR that transforms the entire acoustic model. The classical fMLLR estimation
involves maximization of the likelihood criterion based on individual Gaussian
components statistic.We proposed an approach which takes into account the
overall likelihood of a HMMstate. It estimates the transformation to optimize the
ML criterion of HMM directly using gradient descent algorithm