A study on soft margin estimation of linear regression parameters for speaker adaptation

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.

interspeech09.pdf
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In  Proc. Interspeech

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