# Electroglottograph-Based Speech Emotion Recognition via Cross-Modal Distillation
## 1 Introduction  
Speech is an effective medium to express emotions and attitudes through language. Applications of emotion recognition in speech can be found in many areas [1, 2]. Extracting and recognizing emotional information from speech signals is an important subject to realize more natural human-computer interaction. Speech emotion recognition with deep learning methods aims to extract deep emotion features through artificial neural networks. The majority of speech emotion recognition architectures utilize neural networks such as convolutional neural networks (CNN), recurrent neural networks (RNN), long-short term memory (LSTM), or their combinations [3, 4, 5, 6, 7, 8, 9, 10]. In recent years, in order to obtain higher recognition accuracy, most research has adopted two strategies to enrich the emotion information that one model can obtain. One strategy is to design and apply more complex architectures, such as deep neural networks (DNN). In 2018, [PERSON] [11] proposed an end-to-end continuous speech emotion recognition model to extract features from the raw speech signal based on DNN, and stack a 2-layer long short-term memory (LSTM) to consider the contextual information in the data. Furthermore, in a model also based on DNN, [PERSON] [12] investigated the choices of inputs and two different strategies of giving labels and applied the best combination to the IEMOCAP database. Another approach to improve accuracy is to consider abundant speech features to model the emotion space. In 2020, [PERSON] [13] proposed a speech emotion recognition model with an \"attention long-term short-term memory (LSTM)-attention\" structure, which combined IS09 and Mel-scaled spectrograms. [PERSON] et al. [14] took Mel-frequency Cepstral Coefficients (MFCCs), chromagrams, Mel-scaled spectrograms, Tonnetz representationsand spectral contrast features extracted from speech as inputs and achieved 86.1% recognition accuracy on a 7-class EMO-DB dataset using a deep CNN network. Although the above research obtained good performance in the speech emotion recognition task, it must be pointed out that such performance is acquired at the sacrifice of the model's portability. There have been attempts to overcome the problems of huge model size and feature redundancy. In 2021, [PERSON] [15] jumped out of the traditional method based on machine learning, focusing on high-level features in real value space, and proposed a unique method of feature and network coding using quaternion structure model (QCNN), which not only ensures good accuracy of speech emotion recognition, but also greatly reduces the size of the model. In order to overcome the problem of feature redundancy in speech emotion recognition, [PERSON] [16] applied unsupervised feature selection to a combination of INTERSPEECH 2010 paralinguistic features, Gammatone Cepstral Coefficients (GTCC) and Power Normalized Cepstral Coefficients (PNCC). The Feature Selection with Adaptive Structure Learning (FSASL), Unsupervised Feature Selection with Ordinal Locality (UFSOL) and the novel Subset Feature Selection (SuFS) algorithms were used to reduce the feature dimension of input features to obtain better SER performance. Although these research works have explored methods to simplify the model, they still depend on acoustic features extracted from speech. In other words, clear speech signals are still necessary. However, when it comes to applications in real life, what we need is an efficient model to deal with much more complex scenes. For example, clear speech may be hard to obtain. Aiming at facing these problems and improving the practicability of the model, we seek to design an efficient speech emotion recognition model to realize a comparable performance with less and steadier input (just one) and simpler model architecture. Electroglottograph (EGG) is a signal which can reflect vocal cord movement through recording electrical impedance in the glottis collected by electrodes situated on the throat [17].