Open AccessSpeaker Adaptation Using ICA‐Based Feature Transformation
Author(s) -
Jung HoYoung,
Park Mansoo,
Kim HoiRin,
Hahn Minsoo
Publication year - 2002
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.02.0202.0003
Subject(s) - feature (linguistics) , transformation matrix , transformation (genetics) , pattern recognition (psychology) , independent component analysis , computer science , speech recognition , smoothing , artificial intelligence , adaptation (eye) , computer vision , philosophy , linguistics , physics , biochemistry , chemistry , kinematics , classical mechanics , optics , gene
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 regression‐based 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 ICA‐based 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.