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

Shigeki Matsuda, Yu Tsao, Jinyu Li, Satoshi Nakamura, and Chin-Hui Lee

Abstract

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.

Details

Publication typeInproceedings
Published inProc. Interspeech
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