By analyzing the mechanism of pronunciation, traditional acoustic parameters, including fundamental frequency, Mel frequency cepstral coefficients (MFCC), linear prediction cepstrum coefficient (LPCC), frequency perturbation, amplitude perturbation, and nonlinear characteristic parameters, including entropy (sample entropy, fuzzy entropy, multi-scale entropy), box-counting dimension, intercept and Hurst, are extracted as feature vectors for identification of pathological voice. Seventy-eight normal voice samples and 73 pathological voice samples for /a/, and 78 normal samples and 80 pathological samples for /i/ are recognized based on support vector machine (SVM). The results showed that compared with traditional acoustic parameters, nonlinear characteristic parameters could be well used to distinguish between healthy and pathological voices, and the recognition rates for /a/ were all higher than those for /i/ except for multi-scale entropy. That is why the /a/ sound data is used widely in related research at home and abroad for obtaining better identification of pathological voices. Adopting multi-scale entropy for /i/ could obtain higher recognition rate than /a/ between healthy and pathological samples, which may provide some useful inspiration for evaluating vocal compensatory function.