We propose a new discriminative learning framework, called
soft margin feature extraction (SMFE), for jointly optimizing
the parameters of transformation matrix for feature extraction
and of hidden Markov models (HMMs) for acoustic
modeling. SMFE extends our previous work of soft margin
estimation (SME) to feature extraction. Tested on the
TIDIGITS connected digit recognition task, the proposed
approach achieves a string accuracy of 99.61%, much better
than our previously reported SME results. To our knowledge,
this is the first study on applying the margin-based method in
joint optimization of feature extraction and acoustic modeling.
The excellent performance of SMFE demonstrates the success
of soft margin based method, which targets to obtain both
high accuracy and good model generalization.