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清华大学学报(自然科学版)  2017, Vol. 57 Issue (11): 1159-1162,1169    DOI: 10.16511/j.cnki.qhdxxb.2017.26.060
  计算机科学与技术 本期目录 | 过刊浏览 | 高级检索 |
无声语音接口中超声图像的混合特征提取
路文焕1, 曲悦欣1, 杨亚龙1, 王建荣2, 党建武2
1. 天津大学 软件学院, 天津 300350;
2. 天津大学 计算机科学与技术学院, 天津 300350
Hybrid feature extraction from ultrasound images for a silent speech interface
LU Wenhuan1, QU Yuexin1, YANG Yalong1, WANG Jianrong2, DANG Jianwu2
1. School of Computer Software, Tianjin University, Tianjin 300350, China;
2. School of Computer Science and Technology, Tianjin University, Tianjin 300350, China
全文: PDF(1302 KB)  
输出: BibTeX | EndNote (RIS)      
摘要 在基于超声的无声语音接口实现中,通常使用主成分分析或离散余弦变换提取舌部超声图像的特征。为了保留图像的关键信息,该文提出3种混合特征提取方法:使用主成分分析从小波系数中提取特征(Wavelet PCA)、分块离散余弦变换主成分分析(block DCT-PCA)和分块Walsh Hadamard变换主成分分析(block WHT-PCA)。根据能量选取适量的离散余弦变换或WHT变换系数,使用主成分分析提取选定系数的特征。实验结果表明:该文提出的混合特征提取方法优于主成分分析或离散余弦变换,其中block DCT-PCA方法最优。
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路文焕
曲悦欣
杨亚龙
王建荣
党建武
关键词 无声语音接口超声舌部主成分分析离散余弦变换Walsh-Hadamard变换    
Abstract:Principal component analysis (PCA) and discrete cosine transform (DCT) are used to extract features from ultrasound images to build an ultrasound based silent speech interface. The critical information in the image is presented by using three hybrid feature extraction methods. The first method uses PCA to extract discrete wavelet transform coefficient features. The second and third methods truncate the DCT or Walsh-Hadamard transform coefficients to the appropriate number according to the energy with the truncated coefficients then used by PCA to extract the features. Tests show that this hybrid feature extraction method outperforms standalone PCA or DCT analyses. The block DCT-PCA method gives the best result among all the methods.
Key wordssilent speech interface    ultrasound    tongue    principal component analysis    discrete cosine transform    Walsh-Hadamard transform
收稿日期: 2017-02-23      出版日期: 2017-11-15
ZTFLH:  TP391.4  
通讯作者: 王建荣,副教授,E-mail:wrj@tju.edu.cn     E-mail: wrj@tju.edu.cn
引用本文:   
路文焕, 曲悦欣, 杨亚龙, 王建荣, 党建武. 无声语音接口中超声图像的混合特征提取[J]. 清华大学学报(自然科学版), 2017, 57(11): 1159-1162,1169.
LU Wenhuan, QU Yuexin, YANG Yalong, WANG Jianrong, DANG Jianwu. Hybrid feature extraction from ultrasound images for a silent speech interface. Journal of Tsinghua University(Science and Technology), 2017, 57(11): 1159-1162,1169.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2017.26.060  或          http://jst.tsinghuajournals.com/CN/Y2017/V57/I11/1159
  图1 视觉特征提取过程
  图2 超声图像的一级Haar小波变换
  图3 BlockDCTGPCA 方法的特征提取过程
  表1 不同特征提取方法的识别率
  图4 使用不同维度的DCT和WHT系数的识别率
  图5 混淆矩阵
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