Implementation of an Automatic Meeting Minute Generation System Using YAMNet with Speaker Identification and Keyword Prompts  
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for automatically generating meeting minutes; meanwhile, the system can simultaneously identify the speakers and meeting keywords. During the conference/meeting, accurately segmenting speech for subsequent speaker identification and speech recognition is crucial amidst various background noises. [PERSON] et al. [1] used various CNN architectures to classify the soundtracks of a dataset with 5.24 million hours of training videos, which contain 30,871 video-level labels. The convolutional neural network (CNN) is effective in image recognition and audio classification. This study examined various deep-learning neural networks, including AlexNet [2], VGG [3], Inception [4], and ResNet [5]. The effect of varying the size of the label vocabulary and training sets has been investigated. Experimental results reveal that the analogous CNNs used in image classification perform well in audio classification. Increasing training and label sets can improve performance. For the acoustic event classification, a model using embeddings from those classifiers performs better than raw features on the audio set [6]. Many novel studies have been presented for speaker identification [7; 8; 9; 10; 11]. [PERSON] et al. [7] described the main aspects of automatic speaker recognition (ASR), such as speaker verification, identification, and diarization. The performance of current speaker recognition systems was investigated in this survey. A few unsolved challenges, limitations, and possible improvement methods for speaker recognition were presented. [PERSON] et al. [8] used data augmentation to improve the performance of deep neural network (DNN) embeddings for speaker recognition. The DNN is trained to discriminate speakers so the network can map variable-length utterances to fixed-dimensional embeddings called x-vectors. This study adds noise and reverberation for data augmentation. By comparing the x-vectors and i-vector on speakers in the Wild and NIST SRE 2016 Cantonese, data augmentation benefits the x-vectors while it is not helpful to the i-vectors. [PERSON] et al. [9] proposed fusing Mel-frequency cepstral coefficients (MFCC) and time-based features (T-MFCC) to improve the performance of a text-independent speaker identification system. The T-MFCC features were fed to a deep neural network (DNN). The results reveal that performance outperforms existing baseline MFCC and time-domain features on the LibriSpeech dataset [6]. [PERSON] et al. [10] utilized time-domain and frequency-domain features to increase the robustness of speaker identification in noisy environments. This method uses deep neural networks to analyze raw waveforms and cepstral coefficients, where the network includes CNNs and fully connected neural network layers. [PERSON] et al. [11] proposed an end-to-end framework for speaker identification. This method utilizes a pre-trained DNN mask and voice VGG. Results confirm the technique works well in various emotional conditions by evaluating the Ryerson audio-visual dataset. [PERSON] et al. [12] proposed using CapsNet for speaker identification models in emotional speech signals. This model improves the CNNs by exploiting the spatial association among low-level features. [PERSON] et al. [13] presented a study on the performance of pre-trained CNNs for sound classification. CNNs were designed for image recognition and then extended toward sound recognition by transfer learning. The invested pre-trained CNNs include GoogLeNet, ShuffleNet, SqueezeNet, YAMNet, and VGGish. During the meeting, generating meeting records automatically through speech recognition is essential. [PERSON] et al. [14] proposed using neural network experts for speech recognition. Each expert considers a delimited area of this decision space.