發(fā)布時(shí)間：2018-07-03 02:01

  本文選題：嗓音聲學(xué)分析 + 病態(tài)嗓音　； 參考：《臨床耳鼻咽喉頭頸外科雜志》2017年02期

【摘要】：目的:探討臨床病態(tài)嗓音的特征及計(jì)算機(jī)自動(dòng)識(shí)別病態(tài)嗓音的可行性。方法:選擇129例聲帶息肉患者為病態(tài)嗓音組,同期選取125例社區(qū)正常嗓音人群為對(duì)照組。應(yīng)用Praat軟件采集分析2組病例獲得相關(guān)聲學(xué)參數(shù)值,包括基頻微擾、振幅微擾、諧噪比、信噪比、聲門噪聲。采用該病態(tài)嗓音組與對(duì)照組病例作為神經(jīng)網(wǎng)絡(luò)檢測的訓(xùn)練集和測試集。同樣方法另外收集140例病態(tài)嗓音及正常嗓音數(shù)據(jù)作為驗(yàn)證集。應(yīng)用SPSS Modeler軟件進(jìn)行人工神經(jīng)網(wǎng)絡(luò)建模,計(jì)算模型對(duì)病態(tài)嗓音的識(shí)別率。結(jié)果:本研究根據(jù)不同性別分組計(jì)算,病態(tài)嗓音組在基頻微擾、振幅微擾、聲門噪聲方面數(shù)值比對(duì)照組增大(P0.05),諧噪比、信噪比方面數(shù)值比對(duì)照組減少(P0.05)。人工神經(jīng)網(wǎng)絡(luò)模型對(duì)病態(tài)嗓音的識(shí)別率為75.7%。結(jié)論:客觀嗓音分析有助于病態(tài)嗓音的鑒別,人工神經(jīng)網(wǎng)絡(luò)在病態(tài)嗓音的識(shí)別上準(zhǔn)確率較高,有很好的臨床應(yīng)用價(jià)值。
[Abstract]:Objective: to explore the features of clinical pathological voice and the feasibility of computer automatic recognition of pathological voice. Methods: 129 patients with vocal cord polyps were selected as pathological voice group and 125 normal voice patients as control group. Two groups of cases were collected and analyzed by Praat software to obtain relevant acoustic parameters, including fundamental frequency perturbation, amplitude perturbation, harmonic noise ratio, signal-to-noise ratio and glottic noise. The pathological voice group and the control group were used as the training set and test set for neural network detection. In the same method, 140 samples of abnormal voice and normal voice were collected as validation set. SPSS Modeler software was used to model artificial neural network, and the recognition rate of pathological voice was calculated. Results: according to different sex groups, the fundamental frequency perturbation, amplitude perturbation and glottic noise were higher in the pathological voice group than in the control group (P0.05), the harmonic noise ratio and the signal-to-noise ratio were lower than those in the control group (P0.05). The recognition rate of artificial neural network model for pathological voice is 75.775%. Conclusion: objective voice analysis is helpful to the identification of pathological voice. The accuracy of artificial neural network in the recognition of pathological voice is high and has good clinical application value.
【作者單位】： 復(fù)旦大學(xué)附屬上海市第五人民醫(yī)院耳鼻咽喉科;上海市虹口區(qū)涼城新村街道社區(qū)衛(wèi)生服務(wù)中心;
【基金】：復(fù)旦大學(xué)附屬上海市第五人民醫(yī)院課題(No:2010WYQJ02)
【分類號(hào)】：R767.92

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