## 5 System Implementation
### Audio Driver  
(i). Introduction to the VOCA model  
Speech-driven face and facial animation synthesis are two essential models in digital human construction, which are used to realize the speech and expression functions of the digital human. The VOCA model can quickly realize these two functions, generate realistic facial expressions and mouth movements, and make the 3D virtual teacher image more vivid and natural [20]. In this paper, VOCA is used to realize the functions related to speech interaction such as the expression and mouth movement of the virtual teacher. VOCA has shown absolute superiority among the numerous current methods. VOCA introduces a unique 4D face dataset containing approximately 29 min of 4D scans captured at 60 fps and synchronized audio from 12 speakers. VOCA also provides animator control to change speaking style, identity-related face shape, and pose (i.e., head, chin, and eye rotation) during animation, and is a model that enables realistic 3D facial animation. (ii). VOCA model architecture  
The input of the VOCA model is the target-specific template T and the original speech signal, and the speech features are extracted through DeepSpeech. The output is the 3D mesh of the target. The whole structure adopts an encoder-decoder network structure, as shown in Figure 7 and Table 2. The encoder part consists of four convolutional layers and two fully connected layers, and the decoder is a fully connected layer + linear activation function [21]. Figure 5: Pleasure-Arousal-Dominance model and Valence-Arousal emotion model. Figure 6: Effects of affective computing for virtual teachers. The overall framework process is as follows:  
Speech feature extraction: The speech signal is input into DeepSpeech, and the feature extraction is carried out to obtain a three-dimensional array (dimension is 60T \(\times\) W \(\times\) D), where T is the length of the input speech in seconds; W is the window size (speech overlap window size); and D is the number of letters in the alphabet plus 1 (space label). Encoder: The labels of the input head module are one-hot encoded, added to the extracted speech features (channel stacking), and then fed into the encoder. In the output of the fourth convolutional layer, the one-hot encoding of the head model label is added to the output again, and then the output of the encoder is obtained by passing through the last two layers of FC. Decoder: The output of the encoder is fed into the decoder to obtain the vertex offset of the 3D mesh. Finally, the final 3D mesh is obtained by adding the vertex offset with the vertex value of the template. Animation control: Change the eight-dimensional one-hot vector to change the output style of the character. The VOCA model has the same face mesh topology at the initial position as the FLAME model, so it can be easily extended. model training  
We employ a new dataset of 4D face scans together with speech. The dataset has 12 subjects and 480 sequences of about 3-4 s each, with sentences chosen from an array of standard protocols that maximize phonetic diversity. The 4D scans are captured at 60 fps, and we align a common face template mesh to all the scans, bringing them into correspondence. The raw 3D head data were aligned using the method provided by the FLAME model [22]. Image-based keypoint detection methods can enhance the robustness of alignment. After alignment, each mesh includes 5023 vertices. For all continuations, the neck boundary and ears were automatically repaired and the area around the eyes was