Hybrid feature extraction from ultrasound images for a silent speech interface

doi:10.16511/j.cnki.qhdxxb.2017.26.060

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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.

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

ZTFLH:

TP391.4

Issue Date: 15 November 2017

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	LU Wenhuan
	QU Yuexin
	YANG Yalong
	WANG Jianrong
	DANG Jianwu

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LU Wenhuan,QU Yuexin,YANG Yalong, et al. 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.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2017.26.060 OR http://jst.tsinghuajournals.com/EN/Y2017/V57/I11/1159

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