[Quantitative analysis of pathological voice and identification with artificial neural network]

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.