A Comparative Study of Pathological Voice Based on Traditional Acoustic Characteristics and Nonlinear Features_Journal of Biomedical Engineering

Authors：

GANDeying ,  HUWeiping , ZHAOBingxin

Electronic Engineering College, Guangxi Normal University, Guilin 541004, China;

Corresponding author：

HUWeiping, Email: huwp@gxnu.edu.cn

Keywords：

pathological voice; support vector machine; traditional acoustic feature; nonlinear dynamics technology

DOI：

10.7507/1001-5515.20140217

Video：

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By analyzing the mechanism of pronunciation, traditional acoustic parameters, including fundamental frequency, Mel frequency cepstral coefficients (MFCC), linear prediction cepstrum coefficient (LPCC), frequency perturbation, amplitude perturbation, and nonlinear characteristic parameters, including entropy (sample entropy, fuzzy entropy, multi-scale entropy), box-counting dimension, intercept and Hurst, are extracted as feature vectors for identification of pathological voice. Seventy-eight normal voice samples and 73 pathological voice samples for /a/, and 78 normal samples and 80 pathological samples for /i/ are recognized based on support vector machine (SVM). The results showed that compared with traditional acoustic parameters, nonlinear characteristic parameters could be well used to distinguish between healthy and pathological voices, and the recognition rates for /a/ were all higher than those for /i/ except for multi-scale entropy. That is why the /a/ sound data is used widely in related research at home and abroad for obtaining better identification of pathological voices. Adopting multi-scale entropy for /i/ could obtain higher recognition rate than /a/ between healthy and pathological samples, which may provide some useful inspiration for evaluating vocal compensatory function.

Citation： GANDeying, HUWeiping, ZHAOBingxin. A Comparative Study of Pathological Voice Based on Traditional Acoustic Characteristics and Nonlinear Features. Journal of Biomedical Engineering, 2014, 31(5): 1149-1154. doi: 10.7507/1001-5515.20140217 Copy

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2.	MARKAKI M, STYLIANOU Y. Voice pathology detection and discrimination based on modulation spectral features [J]. IEEE Trans Audio Speech Language Process,2011, 19(7): 1938-1948.
3.	于燕平,胡维平. 病态嗓音特征的小波变换提取及识别研究[J]. 计算机工程与应用, 2009, 45(22): 194-196,205.
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6.	GAO J, YU Y, HU W. Recognition of pathological voices based on fractal theory using Gaussian mixture model[C]//The 2010 International Conference on Information, Electronic and Computer Science. Guilin,China: 2010:1668-1671.
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8.	TING H N, CHIA S Y, KIM K S, et al. Vocal fundamental frequency and perturbation measurements of vowels by normal malaysian Chinese Adults[J].J Voice,2011,25(6):e311-e317.
9.	龚英姬,胡维平. 基于HHT变换的病态嗓音特征提取及识别研究[J]. 计算机工程与应用, 2007, 43(34):217-219.
10.	王修信,胡维平,姚铁钧,等. 频率微扰熵在正常与病态嗓音声谱分析中的应用[J]. 广西师范大学学报,1998,16(4):7-10.
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12.	KILIC M A, OGVT F, DURSUN G, et al. The effects of vowels on voice pertur-bation measures[J]. J Voice, 2004, 18(3): 318-324.
13.	RICHMAN J S, MOORMAN J R. Physiological time-series analysis using approximate entropy and sample entropy [J]. Am J Physiol Heart Circ Physiol, 2000, 278(6):H2039-H2049.
14.	PINCUS S M. Approximate entropy as a measure of system complexity [J]. Proc Nati Acad Sci U S A,1991, 88(6):2297-2301.
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17.	CHEN W T, ZHANG J, YU W X. Measuring complexity using FuzzyEn, ApEn, and SampEn [J].MedEng Phys, 2009, 31(1):61-68.
18.	邹晓阳, 雷敏. 基于多尺度模糊熵的动作表面肌电信号模式识别[J]. 生物医学工程学杂志, 2012, 29(6):1184-1188.
19.	HENRÍQUEZ P, ALONSO J B, FERRER M A, et al. Characterization of healthy and pathological voice through measures based on nonlinear dynamics[J]. IEEE Trans Audio Speech Language Process, 2009, 17(6):1186-1195.
20.	ZHAO G H, HAO M . Incremental learning algorithm of least squares support vector machines based on Renyi entropy [C]//International Conference on Management Science and Engineering. Moscow: 2009:95-100.
21.	THOMPSON C, MULPUR A , MEHTA V, et al. Trandition to chaos in acoustically driven flows[J]. J Acoust Soc Am, 1991, 90(4): 2097-2103.
22.	焦青, 郭永新, 张正国. 混沌与分形及其在心电信号研究中的应用[J]. 生物医学工程学杂志,2009, 26(3):676-680.
23.	高俊芬, 胡维平. 基于非线性动力学和GMM的病态嗓音识别与研究[J].广西师范大学学报(自然科学版), 2011, 29(3): 5-8.
24.	TAKEO F. Box-counting dimension of graphs of generalized takagi series [J]. Japan J Indust Appl, 1996,13: 187-194.
25.	YE X, XIA X, ZHANG J, et al. Effects of trends and seasonalities on robustness of the Hurst parameter estimators [J]. IET Signal Process, 2012, 6(9): 849-856.
26.	VAZIRI G, ALMASGANJ F, JENABI M S. On the fractal self-similarity of laryngeal pathologies detection: the estimation of Hurst parameter [C]//Information Technology and Applications in Biomedicine. Shenzhen: 2008 : 383-386.
27.	CHEN W X, PENG C E, ZHU X, et al. SVM based identification of pathological voices[C]//29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007. Lyon, France: 2007:3786-3789.

1. 胡维平,王修信,曾恩恩,等.噪音频域分析中的特征研究[J]. 广西物理,1998, 19(1):30-33.
2. MARKAKI M, STYLIANOU Y. Voice pathology detection and discrimination based on modulation spectral features [J]. IEEE Trans Audio Speech Language Process,2011, 19(7): 1938-1948.
3. 于燕平,胡维平. 病态嗓音特征的小波变换提取及识别研究[J]. 计算机工程与应用, 2009, 45(22): 194-196,205.
4. 高俊芬,胡维平. 基于非线性动力学和高斯混合模型/支持向量机的病态嗓音识别与研究[J]. 生物医学工程学杂志, 2012, 29(4): 750-753, 759.
5. OROZCO J R, VARGAS J F, ALONSO J B, et al. Voice pathology detection in continuous speech using nonlinear dynamics[C]//The 11th International Conference on Information Sciences, Signal Processing and their Application. Montreal: 2012: 1030-1033.
6. GAO J, YU Y, HU W. Recognition of pathological voices based on fractal theory using Gaussian mixture model[C]//The 2010 International Conference on Information, Electronic and Computer Science. Guilin,China: 2010:1668-1671.
7. HARTL D M, HANS S, VAISSIÈRE J, et al. Objective voice quality analysis before and after onset of unilateral vocal fold paralysis [J]. J Voice, 2010, 15(3): 351-361.
8. TING H N, CHIA S Y, KIM K S, et al. Vocal fundamental frequency and perturbation measurements of vowels by normal malaysian Chinese Adults[J].J Voice,2011,25(6):e311-e317.
9. 龚英姬,胡维平. 基于HHT变换的病态嗓音特征提取及识别研究[J]. 计算机工程与应用, 2007, 43(34):217-219.
10. 王修信,胡维平,姚铁钧,等. 频率微扰熵在正常与病态嗓音声谱分析中的应用[J]. 广西师范大学学报,1998,16(4):7-10.
11. 毛大伟, 曹华, 木拉提·哈米提, 等. 基于梅尔倒谱系数和复杂性的说话人识别[J]. 生物医学工程学杂志, 2006, 23(4):882-886.
12. KILIC M A, OGVT F, DURSUN G, et al. The effects of vowels on voice pertur-bation measures[J]. J Voice, 2004, 18(3): 318-324.
13. RICHMAN J S, MOORMAN J R. Physiological time-series analysis using approximate entropy and sample entropy [J]. Am J Physiol Heart Circ Physiol, 2000, 278(6):H2039-H2049.
14. PINCUS S M. Approximate entropy as a measure of system complexity [J]. Proc Nati Acad Sci U S A,1991, 88(6):2297-2301.
15. COSTA M, GOLDBERGER A L, PENG C K. Multiscale entropy analysis of complex physiologic time series [J].Phys Rev Lett, 2002, 89(6): 1-4.
16. VOICU I, GIRAULT J M. Multi-scale similarity entropy as a new descriptor to differentiate healthy to suffering foetus[C]//International Conference on Complex Systems. Agadir: 2012:1-4.
17. CHEN W T, ZHANG J, YU W X. Measuring complexity using FuzzyEn, ApEn, and SampEn [J].MedEng Phys, 2009, 31(1):61-68.
18. 邹晓阳, 雷敏. 基于多尺度模糊熵的动作表面肌电信号模式识别[J]. 生物医学工程学杂志, 2012, 29(6):1184-1188.
19. HENRÍQUEZ P, ALONSO J B, FERRER M A, et al. Characterization of healthy and pathological voice through measures based on nonlinear dynamics[J]. IEEE Trans Audio Speech Language Process, 2009, 17(6):1186-1195.
20. ZHAO G H, HAO M . Incremental learning algorithm of least squares support vector machines based on Renyi entropy [C]//International Conference on Management Science and Engineering. Moscow: 2009:95-100.
21. THOMPSON C, MULPUR A , MEHTA V, et al. Trandition to chaos in acoustically driven flows[J]. J Acoust Soc Am, 1991, 90(4): 2097-2103.
22. 焦青, 郭永新, 张正国. 混沌与分形及其在心电信号研究中的应用[J]. 生物医学工程学杂志,2009, 26(3):676-680.
23. 高俊芬, 胡维平. 基于非线性动力学和GMM的病态嗓音识别与研究[J].广西师范大学学报(自然科学版), 2011, 29(3): 5-8.
24. TAKEO F. Box-counting dimension of graphs of generalized takagi series [J]. Japan J Indust Appl, 1996,13: 187-194.
25. YE X, XIA X, ZHANG J, et al. Effects of trends and seasonalities on robustness of the Hurst parameter estimators [J]. IET Signal Process, 2012, 6(9): 849-856.
26. VAZIRI G, ALMASGANJ F, JENABI M S. On the fractal self-similarity of laryngeal pathologies detection: the estimation of Hurst parameter [C]//Information Technology and Applications in Biomedicine. Shenzhen: 2008 : 383-386.
27. CHEN W X, PENG C E, ZHU X, et al. SVM based identification of pathological voices[C]//29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007. Lyon, France: 2007:3786-3789.

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