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Di Wu
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Speaker Recognition Based on i-vector and Improved Local Preserving Projection Di Wu; Jie Cao; HuaJin Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 6: June 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

In order to enhance the recognition performance of the i-vector speaker recognition system under unpredicted noise environment, a improved local preserve projection which used for reduce dimension to i-vector is proposed on this paper. First , the non zero eigenvalue is rejected when we solve the optimized objective function, only using the eigenvalue which value greater than zero. A mapping matrix is obtained by solving a generalized eigenvalue problem, so can settle the singular value problem occurred in traditional local preserve projection algorithm.The experiment result shows,The recognition performance of the method proposed in this paper is improved under several kinds of noise environments. DOI : http://dx.doi.org/10.11591/telkomnika.v12i6.3994
Sequence Clustering Algorithm Based on Weighed Sequential Pattern Similarity Di Wu; Jiadong Ren
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 7: July 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i7.pp5529-5536

Abstract

Sequence clustering has become an active issue in the current scientific community. However, the clustering quality is affected heavily by selecting initial clustering centers randomly. In this paper, a new sequence similarity measurement based on weighed sequential patterns is defined. SCWSPS (Sequence Clustering Algorithm Based on Weighed Sequential Pattern Similarity) algorithm is proposed. Sequences with the largest weighted similarity are chosen as the merge objects. The last K-1 synthesis results are deleted from sequence database. Others sequences are divided into K clusters. Moreover, in each cluster, the sequence which has the largest sum of similarities with other sequences is viewed as the updated center. The experimental results and analysis show that the performance of SCWSPS is better than KSPAM and K-means in clustering quality. When the sequence scale is very large, the execution efficiency of SCWSPS is slightly worse than KSPAM and K-means.
Co-Authors HuaJin Wang Jiadong Ren Jie Cao