全卷积循环神经网络的语音情感识别

语音情感识别是人机交互的热门研究领域之一。然而，由于缺乏对语音中时频相关信息的研究，导致情感信息挖掘深度不够。为了更好地挖掘语音中的时频相关信息，提出了一种全卷积循环神经网络模型，采用并行多输入的方式组合不同模型，同时从两个模块中提取不同功能的特征。利用全卷积神经网络（Fully Convolutional Network，FCN）学习语音谱图特征中的时频相关信息，同时，利用长短期记忆（Long Short-Term Memory，LSTM）神经网络来学习语音的帧级特征，以补充模型在FCN学习过程中缺失的时间相关信息，最后，将特征融合后使用分类器进行分类，在两个公开的情感数据集上的测试验证了所提算法的优越性。

Speech emotion recognition based on full convolution recurrent neural network

Speech emotion recognition is one of the hot research fields of human-computer interaction. However, lack of researches on speech time-frequency information leads to the insufficient depth of exploring emotional information. To better explore the time-frequency related information in speech, a novel fully convolutional recurrent neural network model is proposed, in which, the multi-input parallel model combination method is used to extract features of different functions from two modules. The fully convolutional network (FCN) is used to learn the time-frequency related information in the features of speech spectrogram, and long short-term memory neural network (LTSM) is used to learn the frame-level features of speech to supplement the missing time-dependent information during FCN learning. Finally, the features are fused and classified by classifier. Experiments on two public emotional data sets show the superiority of the proposed algorithm.