Smart Doorbell Chinese Voice Interaction Audio Dialogue Dataset

With the popularity of smart homes and devices, users have higher expectations for the voice interaction capabilities of devices like smart doorbells. However, the performance of current voice recognition and dialogue systems in noisy environments is still not ideal, and the recognition accuracy for multiple languages and accents is not high. Existing solutions often lack targeted high-quality audio dataset support, making it challenging to meet the complex interaction needs of real-world scenarios. This dataset aims to improve the recognition accuracy and response capability of smart doorbell voice interactions, addressing technical issues such as voice recognition in noisy environments and natural language understanding faced by the industry. During data collection, we used high-sensitivity microphone arrays to simulate various home environments, including urban streets, indoors, and more, collecting audio dialogue data in multiple languages and accents. Through multiple rounds of annotation, consistency checks, and expert reviews, we ensure high precision and consistency of the data. The annotation team consists of professionals with backgrounds in linguistics and voice processing, and the team has over 30 members. Data preprocessing includes noise reduction, audio segmentation, normalization, and more, finally stored in WAV format and organized by scenario, language, etc. The data storage specifications use grouping, label indexing, and other methods to improve retrieval efficiency. The smart doorbell voice interaction audio dialogue dataset achieves 98% annotation accuracy, featuring completeness and consistency. Innovative use of adaptive data augmentation technology enhances model robustness and introduces new voice feature extraction algorithms, significantly improving voice recognition accuracy in noisy environments. This dataset not only shows significant effects in improving the recognition rate and response speed of existing models but also demonstrates obvious advantages in multi-language and multi-scenario applicability compared to other similar datasets. Its unique scarcity lies in its extensive and high-quality design covering multiple languages and accents. The dataset has good scalability in scale and diversity, suitable for other intelligent audio device application scenarios.