Syn-TurnTurk: A Synthetic Dataset for Turn-Taking Prediction in Turkish Dialogues
Summary: arXiv:2604.13620v1 Announce Type: cross
Effective management of natural dialogue timing presents a notable challenge for voice-based chatbots. Traditional systems often depend on basic silence detection methods, which frequently fall short due to the irregular nature of human speech patterns. Such limitations can result in chatbots interrupting users, ultimately disrupting the conversational flow. This issue becomes even more pronounced in languages like Turkish, where there is a scarcity of high-quality datasets for turn-taking prediction.
To address this gap, the paper introduces Syn-TurnTurk, a synthetic Turkish dialogue dataset developed through the application of various Qwen Large Language Models (LLMs). The dataset is designed to closely simulate realistic verbal exchanges, incorporating elements such as overlaps and strategic silences that are characteristic of natural conversation.
Dataset Generation and Evaluation
The creation of Syn-TurnTurk involved a meticulous process where multiple LLMs were employed to generate dialogue samples that reflect authentic conversational dynamics. This synthetic dataset is essential for training models capable of predicting turn-taking in Turkish dialogues, thereby enhancing the performance of voice-based systems.
Following the generation of the dataset, a comprehensive evaluation was conducted using a variety of traditional and deep learning architectures. The study included the assessment of several models, focusing on their ability to understand and predict turn-taking cues effectively. Among the architectures tested, the results indicated that advanced models, particularly the Bidirectional Long Short-Term Memory (BI-LSTM) and Ensemble methods (Logistic Regression + Random Forest), achieved impressive performance metrics.
- Accuracy: 0.839
- AUC Score: 0.910
These findings underscore the potential of Syn-TurnTurk to significantly improve models’ comprehension of linguistic cues, ultimately facilitating more natural human-machine interactions in the Turkish language.
Implications for the Future
The introduction of Syn-TurnTurk marks a pivotal advancement in the domain of natural language processing (NLP) for Turkish dialogues. By providing a robust dataset for training turn-taking prediction models, this research opens avenues for the development of more sophisticated and responsive voice-based applications.
Future research can leverage Syn-TurnTurk to explore additional dimensions of conversational AI, including sentiment analysis, context-awareness, and the integration of cultural nuances in dialogue management. As the field of AI continues to evolve, the significance of high-quality datasets like Syn-TurnTurk will be paramount in bridging the gap between human communication patterns and machine understanding.
In conclusion, Syn-TurnTurk not only addresses the existing challenges in turn-taking prediction for Turkish dialogues but also sets the stage for further innovations in the realm of conversational AI, enhancing the user experience in voice-based interactions.