# A Feature Fusion Model with Data Augmentation for Speech Emotion Recognition
## 2 Related Work  
After decades of research, speech emotion recognition research has formed a relatively mature process framework and a certain research scale. Before the development of deep-learning methods, the more widely used classic machine-learning models in the field of speech emotion recognition include Naive Bayes classifier, Gaussian Mixture Model (GMM) [30], Hidden Markov Model (HMM) [31], Support Vector Machines (SVM) [32], etc. In recent years, with the development of deep-learning methods, these have been widely used in the field of SER. At present, there is no unified standard for the establishment of the speech emotion database, and the database is often prone to problems such as small data volume and unbalanced data with different labels, which makes the model prone to overfitting. The effective improvement of the impact of data problems on the model is part of our work. Data augmentation is an effective way to prevent overfitting. Mixup is an effective supervised learning method for image data augmentation. It trains the model with a combination of a pair of sample inputs and their targets, making the model more robust to adversarial samples [15]. Mixup can be easily applied to image classification tasks where the target is a single label. The authors in [14] used the Mixup method on the audio scene classification task, and its input was the Log-Mel spectrogram, which improved the Area Under the Curve (AUC) index by 3% compared with the non-Mixup method. MixSpeech [33] improved Mixup and applied it to automatic speech recognition tasks. The input was the Log-Mel spectrogram and MFCC, and the label was a text sequence. It obtained a good Word Error Rate (WER) of 4.7% on the \"Wall Street Journal\" dataset, which is 10.6% higher than the statement error rate of the data augmentation method SpecAugment. It can be seen that the improved Mixup method has achieved good results in enhancing the characteristic data of the two-dimensional audio spectrum, but its application and effectiveness in the original one-dimensional audio data have not been verified. We try to improve the Mixup method and apply it to the original one-dimensional audio to realize data enhancement. Feature extraction is the key to emotion recognition, and the quality of features has a great impact on the performance of SER algorithms [34]. The selection of a dataset with strong emotional representation ability and more robustness from various feature sets is a requirement for researchers before solving the learning model. Deep Neural Networks (DNNs) have shown promising performance in extracting high-level features of SER recent years. [PERSON] [35] experimented with audio waveforms as input, first using CNN to extract representations, and then putting them into LSTM for emotion recognition. [PERSON] et al. [36] applied a Recurrent Neural Network (RNN) to learn the long-term temporal relationship of SER. Since the attention mechanism greatly improves the accuracy of machine translation [37], its application in speech emotion recognition is also widely used. [PERSON] et al. [21] took the Log-Mel spectrogram and its first and second-order difference coefficients as input, and proposed a three-dimensional Convolutional Recurrent Neural Network model (ACRNN) with hierarchical attention for speech emotion classification. [PERSON] et al. [38] proposed a multi-head attention deep-learning speech emotion recognition network based on Logarithmic Mel Filter Bank Energy (LMFBE) spectral features. The design of a model with better recognition performance is also our aim. Another problem with input features is that most studies tend to use only a single spectral feature as input. Only the local feature extraction of emotion in speech is considered, but the coherence of speech as a whole is not considered. Compared with low-level spectral features, there is another type of acoustic feature for speech signal processing called global features, also known as high-level statistical features.