# An Improved Chinese Pause Fillers Prediction Module Based on RoBERTa
## 2 Related Works
### Prediction of Pause Fillers  
Over the years, numerous scholars have extensively researched the prediction of pause fillers using various methods, which has resulted in more natural and authentic text generation. For instance, [PERSON] et al. proposed a method based on analyzing human-robot interaction data and machine learning models to predict the occurrence and appropriate forms of pause fillers, aiming to generate them at the beginning of system utterances in humanoid robot spoken dialog systems, indicating turn-taking or turn-holding intentions [1]. [PERSON] et al. compared two common methods for AD detection on a matched dataset, assessing the advantages of domain knowledge and BERT pre-trained transfer models in predicting pauses and interruptions [2]. [PERSON] et al. obtain a dialog agent with greatly improved linguistic calibration by incorporating such metacognitive features into the training of a controllable generation model [3]. [PERSON] et al. discuss the trajectory of interdisciplinary research on language and the challenges of integrating analysis methods across paradigms, recommending promising future directions for the field along the way [4], and [PERSON] et al. found that in online language processing, disfluent expressions affect listeners' understanding of subsequent nouns, making them more inclined to form associations with objects that have not been previously mentioned, revealing that the fundamental process of decoding language input is influenced by disfluent expressions [5]. These scholars' research in linguistics highlights the importance of pauses, and we aim to enhance linguistic fluency by training a well-versed pause fillers model.  
However, due to individual speaking habits, it is challenging to train a universal pause filler prediction module. To address personalized needs, [PERSON] et al. pro posed a personalized pause filler generation method based on a group prediction model and explored an alternative group prediction approach [6]. It should be noted that there are significant differences between the Chinese and Japanese languages, making the aforementioned models unsuitable for Chinese. To train a Chinese-specific model, new datasets must be sought, and the grouping conditions for pause fillers need to be reexamined. Furthermore, many pause filler prediction models, including those in Japanese, have not considered integration with mainstream speech synthesis systems and overall performance, necessitating further exploration and improvement in practicality [7].  
An accurate and appropriate pause fillers prediction model can automatically predict suitable pause fillers in Text-to-Speech (TTS) systems, simulating the fluency and coherence of natural human speech. This makes the synthesized speech sound more natural and reduces the artificiality of the machine-generated voice. A pause fillers prediction model helps TTS systems better simulate human communication and expression in dialogs [8]. By inserting pause fillers at appropriate positions, TTS systems can better mimic human-to-human conversations, enhancing user experience and making interactions more natural and friendly [9]. Accurately predicting pause fillers can avoid unnecessary pauses and redundancies, thereby improving the efficiency of TTS systems. Speech synthesis can proceed more smoothly, reducing unnecessary delays and waiting times. The pause fillers prediction model can be customized based on individuals' speaking habits and speech characteristics, making the speech synthesis more personalized and adaptable. Different speakers' pause habits can be incorporated into the model, making the synthesized speech more in line with individuals' styles and traits. Combining the pause fillers prediction model with TTS systems significantly enhances the quality, naturalness, and personalization of speech synthesis, making the synthesized speech more closely resemble authentic human expression, ultimately improving user experience and satisfaction.