Hybrid feature extraction from ultrasound images for a silent speech interface

LU Wenhuan; QU Yuexin; YANG Yalong; WANG Jianrong; DANG Jianwu

doi:10.16511/j.cnki.qhdxxb.2017.26.060

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Journal of Tsinghua University(Science and Technology) ›› 2017, Vol. 57 ›› Issue (11) : 1159-1162,1169. DOI: 10.16511/j.cnki.qhdxxb.2017.26.060

COMPUTER SCIENCE AND TECHNOLOGY

Hybrid feature extraction from ultrasound images for a silent speech interface

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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.

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silent speech interface / ultrasound / tongue / principal component analysis / discrete cosine transform / Walsh-Hadamard transform

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LU Wenhuan, QU Yuexin, YANG Yalong, WANG Jianrong, DANG Jianwu. Hybrid feature extraction from ultrasound images for a silent speech interface[J]. Journal of Tsinghua University(Science and Technology). 2017, 57(11): 1159-1162,1169 https://doi.org/10.16511/j.cnki.qhdxxb.2017.26.060

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