Front Desk Service Dialogue Intent Classification Audio Dataset

Currently, the service industry, particularly front desk services, faces the challenge of quickly and accurately identifying customer intent in voice interactions. Traditional dialogue systems typically rely on text input, leading to inaccuracies in recognition under varying voice conditions such as accents and speech rate. This dataset aims to provide high-quality real-world front desk service dialogue audio to help improve the practicality and accuracy of intent classification models. Data is collected using high-sensitivity microphones in actual service desk environments, including service locations of different sizes and background noise complexities. The data undergoes multiple rounds of annotation, covering consistency checks and expert reviews, ensuring high annotation accuracy. The annotation team consists of linguistics and service industry experts, with a team size of 15 people. Data preprocessing includes denoising and volume balancing, and is ultimately stored in WAV format, organized by dialogue themes. The core advantages of this dataset are its high annotation accuracy (95%), consistency (98%), and audio integrity. An innovative introduction of enhancement technology based on audio features improves model adaptability in various environments. It provides solutions to real problems of intent misclassification, significantly enhancing customer satisfaction with dialogue systems. Compared to existing text-based datasets, it offers broader support for voice variations. The data is scarce, covering rare intent types, and has strong generalization capabilities with potential expansion into multilingual and multi-industry environments.