# Electroglottograph-Based Speech Emotion Recognition via Cross-Modal Distillation
## 1 Introduction
The procedure of generating speech can be abstracted as the source-filter model, shown in Figure 1, set up by [PERSON] [18]. It represents speech signals as the combination of a source and a linear acoustic filter, corresponding to the vocal cords and the vocal tract (soft palate, tongue, nasal cavity, oral cavity, etc. ), respectively. As an aspect of the source-filter model, EGG is a credible resource to acquire the periodic source information exactly. Additionally, considering the special acquisition of the EGG signal, it is not affected by mechanical vibrations and noise, which makes it suitable for applications in the real life. In 2017, [PERSON] [19] proved that using the phase of the EGG signal can detect the glottal closure instant (GCI) and glottal opening instant (GOI) within a glottal cycle accurately and robustly, which indicated the advanced performance of the EGG signal in exactly extracting excitation source information of speech signals. In 2016, we realized a text-independent phoneme segmentation combining EGG and speech data, which reflected the superiority of EGG signals regarding robustness to noise [20]. Figure 1: The procedure of generating speech based on the source-filter model. The red part indicates what EGG signals record. As the EGG signal highly corresponds to speaking, a multitude of research has been carried out regarding EGG and its application in the task corresponding to speech [21; 22; 23; 24; 25; 26; 27]. As for figuring out the relationship between EGG and emotions, there are studies that have found a strong base to utilizing EGG signals in speech emotion recognition tasks. In 2015, [PERSON] [28] found that EGG can actually serve to identify emotions between neutral, happy and sad. Based on traditional methods, [PERSON] extracted two classes of speech emotional features from EGG and speech, which were the power-law distribution coefficients (PLDC) and the real discrete cosine transform coefficients of the normalized spectrum of EGG and speech signals [29]. In particular, EGG signals have been utilized to help extract emotion features from speech. In 2010, [PERSON] et al. [30] analyzed changes in incentive source characteristics between different emotions and observed that the fundamental frequency and incentive intensity were related to emotions. Taking the fundamental frequency extracted from the electroglottograph as the ground truth, the features extracted from EGG and speech are compared to verify the effectiveness of extracting excitation source features from speech. Based on this conclusion, [PERSON] et al. [31] studied and proved the effectiveness of incentive intensity in identifying emotions in 2017. Incentive intensity explores and introduces the excitation parameters related to emotion (strength of excitation, SoE, and instantaneous fundamental frequency, \(F_{0}\)). Combined with the MFCC and GMM models, it realizes an emotion recognition model based on speech and electroglottograph signals. However, although [PERSON] proved that EGG signals can help improve performance in the SER task, it still relies on the information from speech signals and cannot realize recognition based on only EGG signals. Cross-modal distillation aims to improve model performance by transferring supervision and knowledge from different modalities. It normally adopts a teacher-student learning mechanism, where the teacher model is usually pre-trained on one modality and then guides the student model on another modality to obtain a similar distribution. The distillation methods usually involve the traditional response-level knowledge distillation [32; 33; 34], which uses the logits as the supervision, and feature-level distillation [35; 36], which encourages the student network to learn and imitate the intermediate representations of the teacher network. For the speech emotion recognition task, in 2018, [PERSON] et al.