Scaled norm-based Euclidean projection for sparse speaker adaptation

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To reduce data storage for speaker adaptive (SA) models, in our previous work, we proposed a sparse speaker adaptation method which can efficiently reduce the number of adapted parameters by using Euclidean projection onto the L1-ball (EPL1) while maintaining recognition performance comparable to maximum a posteriori (MAP) adaptation. In the EPL1-based sparse speaker adaptation framework, however, the adapted Gaussian mean vectors are mostly concentrated on dimensions having large variances because of assuming unit variance for all dimensions. To make EPL1 more flexible, in this paper, we propose scaled norm-based Euclidean projection (SNEP) which can consider dimension-specific variances. By using SNEP, we also propose a new sparse speaker adaptation method which can consider the variances of a speaker-independent model. Our experiments show that the adapted components of mean vectors are evenly distributed in all dimensions, and we can obtain sparsely adapted models with no loss of phone recognition performance from the proposed method compared with MAP adaptation.
Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
Issue Date
2015-12
Language
English
Article Type
Article
Keywords

GRADIENT PROJECTION; REGRESSION

Citation

EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING

ISSN
1687-6180
DOI
10.1186/s13634-015-0290-2
URI
http://hdl.handle.net/10203/205501
Appears in Collection
EE-Journal Papers(저널논문)
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