A recurrent neural network is performed as post-processing. This method investigates the clustering of similar phonetics classes and the imbalanced distribution of samples in the training set to improve accuracy. [PERSON] et al. [15] proposed a spatial-temporal dysarthric speech recognition system. It uses spatial CNN and a multi-head attention transformer to extract speech features. The transformer learns the phoneme shapes. To overcome the small size of the speech database, this method employed transfer learning to maintain the performance by generating synthetic leverage and visuals. [PERSON] et al. [16] developed a multi-modal Mandarin corpus containing air-conducted and bone-conducted speech to form a large corpus for speech recognition. Then, they utilized a multi-modal-conformer speech recognizer to extract semantic embeddings from the air-conducted and bone-conducted speech by a conformer encoder and a transformer decoder. Hence, the semantic embeddings of the two speech sources are fused to improve the recognition performance. [PERSON] et al. (2020) proposed integrating an attention end-to-end model, a hybrid Gaussian mixture model, and a hidden Markov model for speech recognition. In addition, frame-wise time alignment is also used to capture word time stamps, yielding lower latency for online recognition. [PERSON] and [PERSON] (2018) proposed a supervised and unsupervised multi-task learning model for speech recognition. This method uses the pre-trained wav2 vec 2.0 model as a shared encoder and the generative adversarial network into an end-to-end network, which provides a low-resource language speech recognition solution. [PERSON] et al. (2019) proposed using a cross-modal conformer encoder/decoder for conversational speech recognition. This method combines pre-trained speech and text models through an encoder and a mask, yielding rich speech context extraction. This study implements a system for automatically generating meeting minutes; meanwhile, the system can identify the speakers and meeting keywords simultaneously. This system uses a pre-trained Yet Another Mobile Network (YAMNet) convolutional neural network ([PERSON] et al., 2019) incorporated with transfer learning, enabling the YAMNet to identify speakers with a small amount of corpus. The meeting minutes are completed through speech recognition by Google-speech-to-text API. The Jieba word segmentation tool ([PERSON] et al., 2020) generates keywords based on speech recognition results. Accordingly, the proposed system integrates speaker recognition, speech recognition, and critical word recognition for generating meeting minutes and keyword hints during the conference/meeting. In addition, a graphical user interface (GUI) is also provided for practical applications. The major contribution of this study is the integration of advanced technologies of speaker recognition, speech recognition, keyword prompts, and automatic generation of meeting minutes as an application system that can be automatically archived. The remainder of this paper is structured as follows: Section 2 presents the proposed system for generating conference/meeting minutes. Section 3 outlines the experimental findings, while Section 4 provides the concluding remarks.