Recognition of Audio Depression Based on Convolutional Neural Network and Generative Antagonism Network Model

• Published in: IEEE access Vol. 8; pp. 101181 - 101191
• Main Authors: Wang, Zhiyong, Chen, Longxi, Wang, Lifeng, Diao, Guangqiang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Audio data; Convolution; convolutional neural network; Data models; Datasets; Entropy; entropy feature of spectrogram; Feature extraction; Filter banks; generative antagonism network; Hidden Markov models; Machine learning; Matrix algebra; Matrix methods; Mel-scale frequency cepstral coefficients; Neural networks; Recognition; Recognition of audio depression; Root-mean-square errors; Segments; Speech recognition
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2998532

This paper proposes an audio depression recognition method based on convolution neural network and generative antagonism network model. First of all, preprocess the data set, remove the long-term mute segments in the data set, and splice the rest into a new audio file. Then, the features of speech signal, such as Mel-scale Frequency Cepstral Coefficients (MFCCs), short-term energy and spectral entropy, are extracted based on audio difference normalization algorithm. The extracted matrix vector feature data, which represents the unique attributes of the subjects' own voice, is the data base for model training. Then, based on the combination of CNN and GAN, DR AudioNet is used to build the model of depression recognition research. With the help of DR AudioNet, the former model is optimized and the recognition classification is completed through the normalization characteristics of the two adjacent segments before and after the current audio segment. The experimental results on AViD-Corpus and DAIC-WOZ datasets show that the proposed method effectively reduces the depression recognition error compared with other existing methods, and the RMSE and MAE values obtained on the two datasets are better than the comparison algorithm by more than 5%.