English Speech Recognition Training Dataset

In today's rapidly advancing technological era, speech recognition is becoming an important interface for human-computer interaction. However, the performance of existing speech recognition systems in complex environments is still suboptimal. For example, background noise, accent diversity, and different speech patterns continue to challenge existing systems. Existing solutions often lack broad and deep data when handling these variables. This dataset aims to improve the accuracy and stability of speech recognition systems in different scenarios by providing diverse everyday English speech materials. The data collection process used high-sensitivity microphones, with environments including quiet indoors and outdoors, as well as noisy streets. For quality control, multiple rounds of annotation and consistency checks were employed, with an expert team comprising 50 speech recognition researchers and linguists. The data is noise-filtered, segmented, and normalized, stored in WAV format, and organized in a multi-tiered structure for easy retrieval.