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	Deep Neural Networks for Voice Quality Assessment based on the GRBAS Scale
	Simin Xie; Nan Yan; Ping Yu; Manwa L. Ng; Lan Wang; Zhuanzhuan Ji
	2016
会议名称	Interspeech 2016
会议地点	美国旧金山
英文摘要	In the field of speech therapy, perceptual evaluation is widely used by expert listeners as a way to evaluate a pathological and normal voice quality. This approach is understandably subjective as it is subject to listeners’ bias which high inter- and intra-listener variability can be found. As such, research on automatic assessment of pathological voices using a combination of subjective and objective analyses. The present study aimed to develop a complementary automatic assessment system for voice quality based on the well-known GRBAS scale by using an array of multidimensional acoustical measures through Deep Neural Networks. A total of 44 dimensionality measures including Mel Frequency Cepstral Coefficients, Smoothed Cepstral Peak Prominence and Long-Term Average Spectrum was adopted. In addition, the state-of-the-art automatic assessment system based on Modulation Spectrum (MS) features and GMM classifiers was used as comparison system. The classification results using the proposed method revealed a moderate correlation with subjective GRBAS scores of dysphonic severity, and yielded a better performance than the MS-GMM system, with the best accuracy around 81.53%. The findings indicate that such assessment system can be used as an appropriate evaluation tool in determining the presence and severity of voice disorders.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/10040]
专题	深圳先进技术研究院_集成所
作者单位	2016
推荐引用方式 GB/T 7714	Simin Xie,Nan Yan,Ping Yu,et al. Deep Neural Networks for Voice Quality Assessment based on the GRBAS Scale[C]. 见:Interspeech 2016. 美国旧金山.

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