传统声学特征和非线性特征用于病态嗓音的比较研究  

A Comparative Study of Pathological Voice Based on Traditional Acoustic Characteristics and Nonlinear Features

作　　者：甘德英[1] 胡维平[1] 赵冰心[1] 

机构地区：[1]广西师范大学电子工程学院,桂林541004

出　　处：《生物医学工程学杂志》2014年第5期1149-1154,共6页Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(61362003);广西自然科学基金资助项目(2010GXNSFA013128)

摘　　要：本文通过分析嗓音的发音机制,提取正常与病态嗓音的传统声学参数[基频、Mel倒谱系数(MFCC)、线性预测系数(LPCC)、频率微扰、振幅微扰]与非线性动力学特征参数[熵(样本熵、模糊熵、多尺度熵)、计盒维数、计维截距和Hurst参数],作为病态嗓音识别的特征矢量集。应用支持向量机(SVM)对/a/音的78例正常嗓音与73例病态嗓音和/i/音的78例正常嗓音与80例病态嗓音进行建模与识别。结果表明,相对于传统的声学特征参数,非线性特征参数能更好地区分正常与病态嗓音;实验提取的所有参数中,除了多尺度熵,/a/音的正常与病态嗓音的识别率均高于/i/音,因此为了达到识别病态嗓音的目的,国内外相关研究大多采用/a/音数据;多尺度熵特征对/i/音的正常与病态嗓音的识别率较/a/音高,它或能为评价声带代偿功能状态的研究提供有益的启发。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/,and78 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.

关 键 词：病态嗓音 支持向量机 传统声学特征 非线性动力学技术 

分 类 号：R312[医药卫生—基础医学]