Research on the architecture of network text readability rating system based on streaming media
Linlin Zou, Yongquan Li · 2023
In the era of information explosion, language learners can easily obtain a large number of language learning materials. It is a common way to obtain learning materials of the target language learned from streaming media. However, streaming media platforms generally only inspect the compliance of the content in terms of politics and economy, while the inspection of language is relatively lacking. This makes it difficult for language learners to determine whether the text of the streaming media is suitable for their own language proficiency level. Therefore, it is necessary to research on the architecture of network text readability rating system based on streaming media. In this paper, PocketSphinx, SpeechRecognition and other add-on packages based on Python platform have conducted voice recognition on streaming media information collected from user equipment, and then conducted readability grade evaluation on the recognized text, and analyzed the language characteristics of the text, so that language learners can learn corresponding language knowledge.