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Nonlinear acoustic analysis in the evaluation of occupational voice disorders

机译:非线性声学分析在职业语音障碍评估中的应用

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Background: Over recent years numerous papers have stressed that production of voice is subjected to the nonlinear processes, which cause aperiodic vibrations of vocal folds. These vibrations cannot always be characterized by means of conventional acoustic parameters, such as measurements of frequency and amplitude perturbations. Thus, special attention has recently been paid to nonlinear acoustic methods. The aim of this study was to assess the applicability of nonlinear cepstral analysis, including the evaluation of mel cepstral coefficients (MFCC), in diagnosing occupational voice disorders. Material and methods: The study involved 275 voice samples of pathologic voice (sustained vowel "a" and four standardized sentences) registered in female teachers with the occupation-related benign vocal fold masses (BVFM), such as vocal nodules, polyps, and 200 voice samples of normal voices from the control group of females. The mean age of patients and controls was similar (45 vs. 43 years). Voice samples from both groups were analyzed, including MFCC evaluation. Results: MFCC classification using the Sammon Mapping and Support Vector Machines yielded a considerable accuracy of the test. Voice pathologies were detected in 475 registered voice samples: for vowel "a" with 86% sensitivity and 90% specificity, and for the examined sentences the corresponding values varied between 87% and 100%, respectively. Conclusions: Nonlinear voice analysis with application of mel cepstral coefficients could be a useful and objective tool for confirming occupational-related lesions of the glottis. Further studies addressing this problem are being carried out. Med Pr 2013;64(1):29–35
机译:背景技术:近年来,许多论文都强调,声音的产生会受到非线性过程的影响,这会引起人声折叠的非周期性振动。这些振动不能总是通过常规的声学参数来表征,例如频率和幅度扰动的测量。因此,近来特别关注非线性声学方法。这项研究的目的是评估非线性倒谱分析(包括评估mel倒谱系数(MFCC))在诊断职业性语音障碍中的适用性。资料和方法:该研究涉及在女教师中注册的275例病理性语音的语音样本(持续元音“ a”和四个标准句子),这些样本具有与职业有关的良性声带重物(BVFM),例如人声结节,息肉和200来自女性对照组的正常声音的语音样本。患者和对照组的平均年龄相似(45岁对43岁)。分析了两组的语音样本,包括MFCC评估。结果:使用Sammon映射和支持向量机的MFCC分类产生了相当高的测试准确性。在475个注册语音样本中检测到语音病理:对于元音“ a”,灵敏度为86%,特异性为90%,对于所检查的句子,相应的值分别在87%和100%之间变化。结论:应用mel倒谱系数进行非线性语音分析可能是确定声门职业相关病变的有用且客观的工具。解决这个问题的进一步研究正在进行。 Med Pr 2013; 64(1):29–35

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