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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.","2026-02-04 16:16:58","2026-10-19 17:19:57","0.00",786,0,500,"speech recognition dataset, intent classification audio, front desk service dialogue","A front desk service dialogue audio dataset for improving intent classification accuracy.","WAV","1.4G","service industry, language understanding, dialogue systems","intent classification, speech recognition, natural language 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models.",{"id":660,"question":661,"answer":662},3986,"Which industry field is this dataset suitable for?","This dataset is applicable in the language and culture field, particularly in scenarios involving intent recognition in customer service dialogues.",{"id":664,"question":665,"answer":666},3987,"What are the primary uses of the audio data in this dataset?","The audio data is primarily used for training and testing intent classification models, helping to enhance the accuracy and response speed of automated customer service systems.",{"id":668,"question":669,"answer":670},3988,"How does using this audio dataset improve the performance of intent classification models?","This dataset can improve model performance by providing a rich set of dialogue audio samples, helping models to better learn the characteristics of different intents and enhance intent recognition accuracy.",{"id":672,"question":673,"answer":674},3989,"What are the features of this dataset?","The dataset features 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