# Research on Speech Emotion Recognition Method Based A-CapsNet
###### Abstract  
Speech emotion recognition is a crucial work direction in speech recognition. To increase the performance of speech emotion detection, researchers have worked relentlessly to improve data augmentation, feature extraction, and pattern formation. To address the concerns of limited speech data resources and model training overfitting, A-CapsNet, a neural network model based on data augmentation methodologies, is proposed in this research. In order to solve the issue of data scarcity and achieve the goal of data augmentation, the noise from the Noise-92 database is first combined with four different data division methods (emotion-independent random-division, emotion-dependent random-division, emotion-independent cross-validation and emotion-dependent cross-validation methods, abbreviated as EIRD, EDRD, EICV and EDCV, respectively). The database EMODB is then used to analyze and compare the performance of the model proposed in this paper under different signal-to-noise ratios, and the results show that the proposed model and data augmentation are effective. speech emotion recognition; data augmentation; data division; network model +
Footnote †: journal: Medical  
feature extraction and emotion classification. Therefore, constructing an effective acoustic model is a key factor to the success of SER systems [7]. In order for the HCI technique to be timely changed in accordance with the different sorts of emotions, the computer must be able to precisely identify the operator's feelings. The recognition rate was calculated using the hidden Markov model (HMM) in the literature [8]. Literatures [9; 10; 11; 12; 13] use artificial neural networks (ANN), recurrent neural networks (RNN) and convolution neural networks (CNN) to recognize speech emotion. Increasing numbers of models are studied by scholars and the accuracy of recognition can be gradually improved. Among the mentioned models, the HMM is a parametric representation of time-varying features that simulate the process of human language processing, and it needs many samples for time-consuming training [14]. A Gaussian mixture model (GMM) is a probability density estimation model that can fit all probability distribution functions; however, it depends heavily on data distribution or volume, and it is sensitive to data noise [15]. Support vector machines (SVM) map the feature vectors from the input space to a high-dimensional Hilbert space by using kernel tricks at first and then seeking an optimal hyper-plane in the high-dimensional space to classify samples. However, it cannot solve the problem of large-scale training samples that lead to a large or prohibitively large kernel matrix [10]. Deep neural networks have better performance regarding their learning capabilities when handling large-scale data. However, different models have their own pros and cons. For example, an RNN is good at dealing with time series information [16] and a CNN effectively collects spatial data [17]. The most well-known deep learning models are CNN-based, and they perform exceptionally well when learning representations [18]. However, CNNs have two significant flaws, which are as follows: they do not account for significant spatial hierarchies between features, and they do not have rotational invariance [19]. Convolutional layers and pooling layers make up the typical CNN structure. When this happens, the Max-pooling layer, one of the most popular pooling layers, unavoidably removes the information that is not \"max-information,\" which is also important in SER. The convolutional layer takes into account the data inside its receptive field and extracts characteristics in this local region, but it disregards the spatial connections and orientation data within the global region of the emotional speech [20]. To overcome these disadvantages, [PERSON] et al. propose capsule networks (CapsNet) [19], which contain a set of neurons that maintain activity vectors, whose lengths represent presence probabilities and orientations represent entity attributes.