Weighted phone log-likelihood ratio feature for spoken language recognition

doi:10.16511/j.cnki.qhdxxb.2017.25.042

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Abstract The extraction of linguistic discriminative features is one of the fundamental issues in spoken language recognition (SLR). The frame level phone log-likelihood ratio (PLLR) has been recently introduced to improve language recognition. In this paper, the F-ratio analysis method is used to analyze the contributions of different SLR feature vector dimensions. Then, a weighted phone log-likelihood ratio (WPLLR) feature is used to more heavily weight those dimensions with high F-ratio values. Tests on the National Institute of Standards and Technology (NIST) 2007 dataset for SLR show the effectiveness of this feature, with significant relative improvements in the average cost performance and equal error rate compared with the PLLR feature.

Keywords speech signal processing spoken language recognition linguistic discrimination weighted phone log-likelihood ratio (WPLLR) F-ratio

ZTFLH:

TN912.3

Issue Date: 15 October 2017

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	ZHANG Jian
	XU Jie
	BAO Xiuguo
	ZHOU Ruohua
	YAN Yonghong

Cite this article:

ZHANG Jian,XU Jie,BAO Xiuguo, et al. Weighted phone log-likelihood ratio feature for spoken language recognition[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(10): 1038-1041,1047.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2017.25.042 OR http://jst.tsinghuajournals.com/EN/Y2017/V57/I10/1038

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