Psychosis speech recognition algorithm based on deep embedded sparse stacked autoencoder and manifold ensemble - Zhang, Yi; Qin, Xiaolin; Lin, Yuan; Li, Yongming; Wang, Pin; Zhang, Zuwei; Li, Xiaofei (Sheng wu yi xue gong cheng xue za zhi, 2021)
Speech feature learning is the core and key of speech recognition method for mental illness. Deep feature learning can automatically extract speech features, but it is limited by the problem of small samples. Traditional feature extraction (original features) can avoid the impact of small samples, but it relies heavily on experience and is poorly adaptive. To solve this problem, this paper proposes a deep embedded hybrid feature sparse stack autoencoder manifold ensemble algorithm. Firstly, based on the prior knowledge, the psychotic speech features are extracted, and the original features are constructed. Secondly, the original features are embedded in the sparse stack autoencoder (deep network), and the output of the hidden layer is filtered to enhance the complementarity between the deep features and the original features. Third, the L1 regularization feature selection mechanism is designed to compress the dimensions of the mixed feature set composed of deep features and original features. Finally, a weigh