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Automatic Assessment of Pathological Voice Quality Using Multidimensional Acoustic Analysis Based on the GRBAS Scale

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Abstract

Despite the fact that perceptual evaluation is considered as a gold standard for assessing pathological voice quality, the considerably high inter- and intra-listeners variability associated with different perceptual ratings cannot be ignored. This is probably due to other confounding factors such as listeners’ perceptual bias, listeners’ experience and type of rating scale being used. Automatic objective assessment can serve as a useful tool for diagnosis of pathological voices. Acoustic analysis can be useful in determining severity of dysphonia. The present study aimed to develop a complementary automatic voice assessment system by using multidimensional acoustical measures based on the well-known GRBAS perceptual rating scale. A total of 65 dimensionality measures including traditional acoustic methods, MFCC, Glottal-to-Noise Excitation Methods and nonlinear dynamical analysis were used to compose a matrix of features. To reduce redundancy in features, four different feature extraction techniques were applied. The multiclass classification was carried out by means of RBF kernel-SVM and Extreme Learning Machine. The classification results were moderately correlated with GRBAS ratings of severity, with the best accuracy around 77.55 and 80.58 %, respectively. This suggests that such multidimensional acoustic analysis can be an appropriate assessment tool in determining the presence and severity of voice disorders.

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Acknowledgments

The research was partially supported by a grant from National Natural Science Foundation of China (NSFC 61135003 and NFSC 61401452), Shenzhen Speech Rehabilitation Technology Laboratory and Guangdong Innovative Research Team Program (No.201001D0104648280).

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Authors and Affiliations

  1. Shenzhen Speech Rehabilitation Technology Laboratory, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences /The Chinese University of Hong Kong, Shenzhen, China

    Zhijian Wang, Nan Yan & Lan Wang

  2. Department of Otorhinolaryngology Head and Neck Surgery, Chinese People’s Liberation Army General Hospital, Beijing, 100853, China

    Ping Yu

  3. School of Software Engineering, University of Science and Technology of China, Hefei, China

    Zhijian Wang

  4. Speech Science Laboratory, University of Hong Kong, Hong Kong, China

    Manwa L. Ng

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  1. Zhijian Wang
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  2. Ping Yu
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  3. Nan Yan
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  4. Lan Wang
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  5. Manwa L. Ng
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Correspondence to Nan Yan.

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Wang, Z., Yu, P., Yan, N. et al. Automatic Assessment of Pathological Voice Quality Using Multidimensional Acoustic Analysis Based on the GRBAS Scale. J Sign Process Syst 82, 241–251 (2016). https://doi.org/10.1007/s11265-015-1016-2

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  • DOI: https://doi.org/10.1007/s11265-015-1016-2

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