Research on English Blended Learning Based on Speech Sensor Recognition and Data Analysis

With the rapid development of technology, English learning and education are gradually transitioning towards digitalization. Speech sensing recognition technology has brought new possibilities to English teaching. The aim of this study is to explore a blended learning method for English based on speech sensor recognition and data analysis, in order to improve learning effectiveness and student learning experience. Based on the goals of English learning and the needs of students, research is conducted to arrange speech sensors in the learning environment to ensure that they can capture students' speech information. By using speech sensor technology, collect students' speech data and their behavioral data during the learning process to evaluate their learning effectiveness. By tracking the learning process of students, problems can be identified and solved in a timely manner, and indicators such as improvement in pronunciation, learning motivation, and participation can be analyzed to comprehensively understand the effectiveness of teaching methods and student learning performance. By combining speech sensor technology with English learning and utilizing data analysis technology to deeply track and evaluate students' learning process, this comprehensive application provides strong support for blended learning in English, helping to improve learning effectiveness and promote continuous innovation in education.