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Phonetic-class based correlation analysis for severity of dysphonia

机译:基于语音分类的语音障碍严重程度相关分析

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The main purpose of the research is to model the cognitive processes that occur when the physician determines the severity of the dysphonia, and to build an IT system that can substitute the subjective severity diagnosis used by a clinician. In this preliminary study the relationship between acoustic parameters and the speech defect severity determined by a clinician is investigated. Being limited in the number of pathological speech samples, it is very important to choose the effective parameters. After a phoneme level segmentation, acoustic parameters were measured at a predetermined fixed points in continuous speech. Parameters were grouped according to the phonetic classes (classes according to the manner of articulation), and the correlation of the grouped parameters with the severity of dysphonia given by the RBH scale was examined, where R stands for roughness, B for breathiness, H for overall hoarseness. The analysis was carried out on a database containing several pathological disease types, the most frequent being recurrent paresis and functional dysphonia. It was found that beyond the initial acoustic parameters such as jitter(ddp), shimmer(dda), Harmonics-to-Noise Ratio (HNR) and mel-frequency cepstral coefficients (mfcc) measured on vowels, it is worth measuring Soft Phonation Index (SPI) and Empirical mode decomposition (EMD) based frequency band ratios on different phonetic classes. These measures were found to correlate with the severity of dysphonia, determined by the clinician (RBH). They provide useful information and could be useful to differentiate different types of dysphonia like functional dysphonia and recurrent paresis.
机译:该研究的主要目的是对当医生确定发声困难的严重程度时发生的认知过程进行建模,并构建一个可以代替临床医生使用的主观严重程度诊断的IT系统。在这项初步研究中,研究了声学参数与临床医生确定的言语缺陷严重程度之间的关系。由于病理语音样本的数量有限,选择有效参数非常重要。在音素水平分割之后,在连续语音中的预定固定点处测量声学参数。根据语音分类对参数进行分组(根据发音方式进行分类),并检查分组参数与RBH量表给出的发音障碍严重程度的相关性,其中R代表粗糙度,B代表呼吸,H代表整体声音嘶哑。该分析是在包含几种病理疾病类型的数据库中进行的,最常见的是复发性轻瘫和功能障碍。结果发现,除了在元音上测量的初始声学参数(例如,抖动(ddp),闪光(dda),谐波与噪声比(HNR)和梅尔频率倒谱系数(mfcc)”之外,还有必要测量软声指数(SPI)和经验模式分解(EMD)基于不同语音类别的频段比率。发现这些措施与由临床医生(RBH)确定的发声困难的严重程度相关。它们提供了有用的信息,并可能有助于区分不同类型的功能障碍和复发性轻瘫。

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