病态嗓音的定量分析及人工神经网络识别  

Quantitative analysis of pathological voice and identification with artificial neural network

作　　者：庞宇峰[1] 黄娟[1] 徐蓓峥 龚静蓉[1] 邹阳[1] 何双珠 

机构地区：[1]复旦大学附属上海市第五人民医院耳鼻咽喉科,上海200240 [2]上海市虹口区凉城新村街道社区卫生服务中心

出　　处：《临床耳鼻咽喉头颈外科杂志》2017年第2期100-102,共3页Journal of Clinical Otorhinolaryngology Head And Neck Surgery

基　　金：复旦大学附属上海市第五人民医院课题(No:2010WYQJ02)

摘　　要：目的:探讨临床病态嗓音的特征及计算机自动识别病态嗓音的可行性。方法:选择129例声带息肉患者为病态嗓音组,同期选取125例社区正常嗓音人群为对照组。应用Praat软件采集分析2组病例获得相关声学参数值,包括基频微扰、振幅微扰、谐噪比、信噪比、声门噪声。采用该病态嗓音组与对照组病例作为神经网络检测的训练集和测试集。同样方法另外收集140例病态嗓音及正常嗓音数据作为验证集。应用SPSS Modeler软件进行人工神经网络建模,计算模型对病态嗓音的识别率。结果:本研究根据不同性别分组计算,病态嗓音组在基频微扰、振幅微扰、声门噪声方面数值比对照组增大(P<0.05),谐噪比、信噪比方面数值比对照组减少(P<0.05)。人工神经网络模型对病态嗓音的识别率为75.7%。结论:客观嗓音分析有助于病态嗓音的鉴别,人工神经网络在病态嗓音的识别上准确率较高,有很好的临床应用价值。Objective：To discuss the characteristic of the clinical pathological voice and the feasibility of computer automatic identification of pathological voice.Method：A total of 129 clinical patients with polyp of vocal cord were selected as the pathological voice group,while a total of 125 people with normal voice were selected from the community as the control group.Praat software was used to collect and analyze the related acoustic parameter values of two groups of cases,including Jitter,Shimmer,harmonic to noise ratio（HNR）,signal to noise ratio（SNR）,and normalized noise energy（NNE）.Pathological voice group and control group were used as training set and testing set for neural network testing,and another 140 cases of pathological voice and normal voice data were selected as a validation set.SPSS Modeler was used for artificial neural network reconstruction to calculate the identification rate of pathological voice.Result：This study found according to the calculation of groups with different genders that Jitter,Shimmer and NNE were increased in pathological voice group compared with the normal group（P〈0.05）,while HNR and SNR were decreased compared with the normal group（P〈0.05）.Recognition rate of artificial neural network model on pathological voice is 75.7%.Conclusion：Objective voice analysis is helpful in the identification of pathological voice.Artificial neural network has higher accuracy in recognition of pathological voice,with good clinical application value.

关 键 词：嗓音声学分析 病态嗓音 人工神经网络 

分 类 号：R767.92[医药卫生—耳鼻咽喉科]