Avian influenza influences the economy, food safety and human health. A rapid and accurate detection of chicken infected with avian influenza in farming not only directly benefits the chicken farming, but also prevents the cross propagation of avian influenza. This paper proposes a non-invasive disease poultry detection method based on voice analysis, which is designed to achieve the identification of the voice of chickens infected with avian influenza and that of the healthy ones. First, 14 white leghorn chickens of 5 weeks of age with specific pathogen free (SPF) were put into the isolated cage in the animal biosafety level 3 (ABSL 3) laboratory to record their voice. The voice samples of healthy chickens were collected by a T&F-91 enhanced 32G digital HD recording pen, and then the chickens were inoculated with the H7N9 avian influenza virus in the ABSL-3 laboratory. The H7N9 subtype avian influenza virus was diluted to 106EID50/0.1 mL with 10 000 ?/mL penicillin and streptomycin free phosphate-buffered saline (PBS), which was then used to inoculate the chickens, each with 0.1 mL virus diluent. After that, the samples of infected chickens' voice were collected. Secondly, in light of the fact that the frequency of chickens' voice signal was higher than the ambient noise, the recorded voice signal was processed with pre-emphasis. The high pass filter was used, so as to weaken the signal of the noise and improve that of chickens' voice. Thirdly, the processed chicken voice signal was further treated with the hamming window, and then it was divided into smaller segment, 21.3 ms per frames, which could be regarded as quasi steady state process. Fourthly, because the spectral entropy values of the obtained chickens' voice and the noise were significantly distinguishing, the values of each frame were calculated out. Based on these values, the end point detection method was put forward, so that the chickens' voice fragments were extracted from the complex ambient noise-containing record, while the non-chicken voice was discarded. Fifthly, the extracted chickens' voice fragments were treated with time domain analysis, and 3 attributes (short time zero crossing rate, short time energy and the combination of them) were figured out as the characteristics of the healthy chickens and chickens infected with avian influenza. The 450 sampling voice of the healthy chickens and 450 of chicken infected with avian influenza were marked before their order being randomly disrupted. The marked samples were divided into 4 groups: 1 training set (600 samples) and 3 testing sets (100 samples in each group). Finally, the training set was trained by 3 Takagi-Sugeno (T-S) fuzzy neural networks (each with different types of the membership function: π function, Gaussian function and Bell function). It was revealed from the training result that the network with the bell function had the highest recognition rate. So the network with bell shape function was applied to the 3 testing sets and results were obtained respectively: the sensitivity was 75.47%, 80.39% and 76.92%, the specificity was 80.85%, 79.59% and 72.92%, and the true recognition rate was 78%, 80% and 75%. Therefore, this kind of detection method might provide a set of non-invasive, rapid and efficient methods for avian influenza infected chickens detection or identification in poultry farms and poultry circulation market.