Speaker Adaptation Using ICA-Based Feature Transformation

다국어 초록 (Multilingual Abstract)

Speaker adaptation techniques are generally
used to reduce speaker differences in speech recognition. In
this work, we focus on the features fitted to a linear regressionbased
speaker adaptation. These are obtained by feature
transformation based on independent component analysis
(ICA), and the feature transformation matrices are estimated
from the training data and adaptation data. Since the
adaptation data is not sufficient to reliably estimate the ICAbased
feature transformation matrix, it is necessary to adjust
the ICA-based feature transformation matrix estimated from a
new speaker utterance. To cope with this problem, we propose
a smoothing method through a linear interpolation between the
speaker-independent (SI) feature transformation matrix and the
speaker-dependent (SD) feature transformation matrix. From
our experiments, we observed that the proposed method is
more effective in the mismatched case. In the mismatched case,
the adaptation performance is improved because the smoothed
feature transformation matrix makes speaker adaptation using
noisy speech more robust.