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Title: [Detection of speech pathology based on parameters of analysis of dysphonia in speech and voice].
Author: Wei M, DU J, Geng L, Wang W.
Journal: Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za Zhi; 2022 Jul; 36(7):492-496. PubMed ID: 35822373.
Abstract:
Objective:To analysis speech pathology based on dysphonia in speech and voice（ADSV）. Methods:The acoustic signals of continuous vowels and continuous speech of one-hundred and thirteen individuals were collected, including 93 vocal cord polyps cases, 20 glottis laryngeal carcinoma cases and 47 volunteers without speech sound disorders. Cepstral peak prominence（CPP）, CPP standard deviation（CPP SD）, L/H spectral ratio（L/H ratio）, L/H ratio standard deviation（L/H ratio SD） and cepstral/spectral index of dysphonia（CSID） were analyzed by ADSV to explore the role of these parameters in the recognition of speech pathology. Results:In the acoustic signal of continuous vowels, CPP and L/H ratio in normal group were higher than those in pathological voice group（P<0.001）, while CPP SD and CSID were lower than those in pathological voice group（P<0.001）, CPP and CSID areas under ROC curve were 0.95 and 0.99, respectively, which were important acoustic parameters for diagnosing pathological voice. In continuous speech acoustic signals, CPP, CPP SD and L/H ratio in the normal group were all higher than those in the speech disorders group（P<0.001）, and the area under the curve of CPP SD was 0.90, which showed high accuracy in diagnosing pathological voice. The ADSV voice analysis parameters CPP, CPP SD, CSID, and L/H ratio also showed significant differences between the vocal cord polyp group and the glottic laryngeal cancer group. The results of the discriminant analysis model show that the use of ADSV voice parameters can distinguish vocal cord polyps and laryngeal cancers. Conclusion:The ADSV voice analysis parameters can not only distinguish the voice signals of the normal group and the pathological group, but also distinguish different types of pathological voices. It has high sensitivity and specificity in diagnosing pathological voices. 目的：利用发声与言语嗓音障碍分析（ADSV）软件对病理嗓音进行检测，明确ADSV参数在识别病理嗓音中的作用。 方法：分别采集113例病理嗓音患者（声带息肉患者93例、声门型喉癌患者20例）以及47例嗓音正常志愿者的持续元音和连续言语的声学信号，采用ADSV分析各组在持续元音和连续言语下的嗓音参数：倒频谱峰值（CPP）、倒频谱峰值标准差（CPP SD）、低/高频谱比（L/H ratio）、低/高频谱比标准差（L/H ratio SD）及嗓音障碍倒频谱/频谱指数（CSID），探究这些参数在识别病理嗓音中的作用。 结果：在持续元音声学信号中，正常组的CPP、L/H ratio值均大于病理嗓音组（P<0.001），CPP SD、CSID则小于病理嗓音组（P<0.001），其中CPP、CSID的ROC曲线下面积分别为0.95、0.99，是诊断病理嗓音的重要声学参数。在连续言语声学信号中，正常组的CPP、CPP SD、L/H ratio均大于病理嗓音组（P<0.001），其中CPP SD的曲线下面积为0.90，对于诊断病理嗓音具有较高的准确性。同时ADSV嗓音分析参数CPP、CPP SD、CSID、L/H ratio在声带息肉组和声门型喉癌组之间的差异也有统计学意义（P<0.05）。判别分析模型结果显示利用ADSV嗓音参数不仅能够区别病理嗓音，而且能够较好地区分声带息肉和喉癌。 结论：ADSV嗓音分析参数不仅能够区分正常与病理嗓音信号，而且还能区分不同类型的病理嗓音，其在诊断病理嗓音上具有较高的敏感性和特异性。.

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