## 2 Proposed Conference/Meeting Minute Generation System
### Speaker YAMNet  
YAMNet is an 86-layer deep learning neural network, with layer 1 as the image input layer, layers 2 to 83 as the convolutional layer, layer 84 as the fully connected layer, and layer 86 as the recognition layer. Therefore, YAMNet has an intense convolutional layer, which can effectively analyze the spectrum variations in the spectrogram so that the fully connected layer can recognize the speakers. YAMNet is a pre-trained model with 86 layers of neural network; each layer's detailed functions and specifications are shown in Table 1.  
YAMNet uses AudioSet, an open-source corpus, to learn 521 categorizations, and mobile devices can run the model. To match speech spectral features with human ear perception, YAMNet uses Mel-frequency cepstral coefficients (MFCC) as features and combines several frames to form a Mel cepstral spectrogram, which is used as the input image of YAMNet. In this study, we use the Libri Speech open-source corpus as the training data, an English-based corpus containing 1000 h of speech data sampled at 16 kHz and after careful alignment and cutting. The corpus is categorized into clean speech and noisy speech. In order to speed up the model training, we only use 100 h of clean data for YAMNet training and set 80% of the training data as the training set and the remaining 20% as the validation set. The frame length is 1600, and four frames are grouped together to form a short speech segment. It is helpful to view the trajectory changes. Each speech frame is transformed to the frequency domain by the Fast Fourier Transform (FFT), given as  
\[S(m,f)=\sum_{t=0}^{N-1}s(t+mM)\cdot h(t)\cdot e^{-j(2\pi/N)ft} \tag{1}\]  
where \(f\) denotes the frequency index, \(h(t)\) is the Hanning window of size \(N\) (512), and \(M\) (256) is the frame shift. In turn, the spectral magnitude is calculated. Filtering the spectral magnitude by the Mel filter banks obtains the Mel spectrograms \(S_{Mel}(m,f)\), which the _melSpectrogram_ function in Matlab with version R2024a or later can extract. To prevent the log Mel spectrum from being infinite, we add a small value \(e\) in the denominator, given as  
\[S^{\prime}_{Mel}(m,f)=\log(S_{Mel}(m,f)+\epsilon) \tag{2}\]  
where \(\epsilon\) is empirically chosen to 0.001. Figure 1: Block diagram of the proposed conference/meeting minute generation system. The log Mel spectrogram \(S^{\prime}_{Mel}(m,f)\) is input into the YAMNet neural network. After the features are intercepted in the convolutional layer, the result goes to the pooling layer to retain the maximum features of the speakers in the speech signal. Finally, the fully connected layer is used as a classifier to identify the speakers. To achieve the goal of Chinese speaker identification, we first train the YAMNet model with the Libri Speech open-source corpus in English, then use the pre-train YAMNeT model through transfer learning, where the 84-86 th network layers are re-trained by the Chinese corpus for identifying Mandarin Chinese speakers. The corpus is divided into 80% for the training set and 20% for the validation set. VGGish [1] is another deep sound CNN based on the VGG. It can be applied to speaker identification through transfer learning. The comparisons between YAMNet and VGGish are shown in Table 1.  
VGGish extracts audio features based on the architecture of the VGG network, which consists of a convolutional layer and a fully connected layer. The main structure of VGGish consists of convolutional layers and fully connected layers. VGGish has four convolutional blocks; each contains 2 to 3 layers, totaling 13 convolutional layers and 3 fully connected layers. YAMNet, a lightweight model based on MobileNet V1, is a practical choice for fast recognition applications. Its 26 convolutional layers, primarily depth-separable, ensure efficient feature acquisition. The architecture is completed with a single fully connected layer, enhancing its practicality.