| Popis: |
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 weighted local preserving projection algorithm and an ensemble learning mechanism are designed, and a manifold projection classifier ensemble model is constructed, which further improves the classification stability of feature fusion under small samples. In addition, this paper designs a medium-to-large-scale psychotic speech collection program for the first time, collects and constructs a large-scale Chinese psychotic speech database for the verification of psychotic speech recognition algorithms. The experimental results show that the main innovation of the algorithm is effective, and the classification accuracy is better than other representative algorithms, and the maximum improvement is 3.3%. In conclusion, this paper proposes a new method of psychotic speech recognition based on embedded mixed sparse stack autoencoder and manifold ensemble, which effectively improves the recognition rate of psychotic speech.语音特征学习是精神病语音识别方法的核心和关键。深层特征学习可以自动提取语音特征，但受限于小样本问题；传统的特征提取（原始特征）避免小样本问题影响，但严重依赖经验且自适应不佳。为了解决这一问题，本文提出了一种深层内嵌混合稀疏堆栈自动编码器流形集成算法。首先，基于先验知识提取精神病语音特征，构造原始特征。其次，将原始特征内嵌入到稀疏堆栈自动编码器（深度网络）中，对隐藏层的输出进行滤波，增强深层特征与原始特征的互补性。再次，设计 L1 正则化特征选择机制，压缩由深层特征和原始特征组成的混合特征集的维度。最后，设计了加权局部保持投影算法和集成学习机制，构造了流形投影分类器集成模型，进一步提高了小样本下特征融合的分类稳定性。此外，本文首次设计了一个中大规模的精神病语音采集方案，收集并构建了一个大规模的中文精神病语音数据库，用于精神病语音识别算法的验证。实验结果表明，该算法主要创新部分有效；与其他有代表性的算法相比具有更好的分类准确率，最大改善了 3.3%。综上所述，本文提出了一种基于深层内嵌混合稀疏堆栈自动编码器和流形集成的精神病语音识别方法，有效提高了精神病语音识别准确率。. |
