We formulate a framework for soft margin estimation-based
linear regression (SMELR) and apply it to supervised speaker
adaptation. Enhanced separation capability and increased discriminative
ability are two key properties in margin-based discriminative
training. For the adaptation process to be able to
flexibly utilize any amount of data, we also propose a novel interpolation
scheme to linearly combine the speaker independent
(SI) and speaker adaptive SMELR (SMELR/SA) models. The
two proposed SMELR algorithms were evaluated on a Japanese
large vocabulary continuous speech recognition task. Both the
SMELR and interpolated SI+SMELR/SA techniques showed
improved speech adaptation performance in comparison with
the well-known maximum likelihood linear regression (MLLR)
method. We also found that the interpolation framework works
even more effectively than SMELR when the amount of adaptation
data is relatively small.