# An Ensemble Model for Multi-Level Speech Emotion Recognition
## 1 Introduction  
As the most convenient and natural medium for human communication, speech is the most basic and direct way we have to transmit information to each other. Speech contains a variety of information, and can express rich emotional information through the emotions that it carries and its perception in response to objects, scenes, or events. As an important research direction for speech signals, Speech Emotion Recognition (SER) is key for computers to understand human emotions and is the premise of human-computer interactions [1].  
Speech emotion recognition aims to identify the correct emotional state of the speaker through the speech signal. At present, research on emotion is still an interdisciplinary field, and it has not yetbeen uniformly defined and standardized. Since speech is not a complete expression of emotional physiological signals, how to efficiently and accurately recognize the emotions expressed by people while ignoring other sensory information is a hotspot in the field of phonetics research in recent years.  
At present, the recognition rate of speech emotions is low, and the generalization ability is not strong. It mainly comes from the following aspects:  
First of all, the features involved in existing emotion recognition are mainly global features [2]. Even if corresponding statistical functions are added, there is still less consideration of the temporal characteristics of speech. Existing research is based on specific languages, and emotion recognition based on this idea is not universal. The decision-making aspect of the speech emotion recognition model often plays a decisive role. At this time, if the state of the expert is unstable, it directly affects the final emotional judgment. The speech emotion recognition model often involves the traditional pattern recognition method. Since there is no universal speech emotion model at present, the accuracy of the model is greatly affected. In addition, the corpus available for testing is limited in size and number, making it difficult to find a fully matched comprehensive training model.  
Based on the above research status, although some scholars are working to overcome these problems to improve the recognition rate of speech emotions, few experts have fully explored the correlation between global and local features in different roles, features, and models. Focusing on the above problems, this paper carries out related research on the design of speech emotion features with a multi-level deep learning model and constructed ensemble learning schemes for the comprehensive consideration of multi experts' suggestions [3]. A more stable ensemble learning model with comprehensive generalization ability and evaluation of speech emotions is proposed.  
The second part of the paper focuses on a feasibility analysis of popular acoustic feature extraction methods and related deep learning models. In the third part, three experts in speech emotion recognition are designed according to different angles describing the acoustic characteristics. Each expert, also known as a classifier, separately extracts multidimensional features and designs a single-/multiple-level deep learning model that meets its characteristics, and then trains them separately. At the same time, an ensemble learning program is designed to enable the three experts to evaluate speech emotion from different sides. The fourth part lists the results of model training and testing of the three experts. Based on a comparison of their respective performances, the ensemble learning test is conducted. The fifth part is the summary of the work of this paper and the prospects for future work.