Dnn-hmm based acoustic model for continuous pig cough sound recognition

Abstract

To detect the respiratory disease through pig cough sound in the early stage, a novel method based on Deep Neural Networks-Hidden Markov Model (DNN-HMM) was proposed to construct an acoustic model for continuous pig cough sound recognition. Noises in the continuous pig sounds were eliminated by the Wiener algorithm based on wavelet thresholding the multitaper spectrum, and the experimental corpus was obtained from the denoised continuous pig sounds. The 39-dimensional Mel Frequency Cepstral Coefficients (MFCC) extracted from the corpus were considered as feature vectors. Sounds in pig farms were divided into pig coughs, non-pig coughs, and silence segments. In the HMM, the number of hidden states of pig cough, non-pig cough and silence segments were 5, 5 and 3 respectively, and the observation states represented the feature vectors of the continuous pig sound signal. Based on experiments and empirical theory, the DNN model with 3 hidden layers and 100 nodes per layer was used to describe the correspondence between hidden states and observation serials. Through experiments, the context frames of DNN input were set to 5. Under the condition of optimal parameter setting, the traditional acoustic model Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) was compared with DNN-HMM through a 5-fold cross-validation experiment. It was found that the Word Error Rate (WER) of each group in DNN-HMM was lower than that in GMM-HMM, and the average WER was 3.45% lower. At the same time, the best result of the DNN-HMM model was obtained with the lowest WER of 7.54%, and the average WER was 8.03%. The results showed that the method of DNN-HMM based acoustic model for continuous pig cough sound recognition was stable and reliable.