Speech Recognition Enhancement and Compression Perception in Russian Translation Teaching Cooperative System Application

Abstract

Abstract In the digital age, characterized by the ubiquity of big data and artificial intelligence, Russian translation education is poised for transformative change. This study explores the integration of advanced speech recognition and compressed perception technologies into Russian translation instruction, highlighting their potential to elevate educational quality and translator training. Utilizing the Spring Boot framework, we developed a comprehensive Russian translation teaching system, incorporating a Russian-Chinese speech translation model that leverages the RNN Transducer model and an N-gram language model, alongside compressed perception theory for efficient speech signal processing. Our findings reveal a notable enhancement in Russian speech recognition accuracy and processing speed, with a 4.73% improvement in endto-end speech detection accuracy and a significant reduction in word error rate, outperforming CTC and CTC-LM models by 14.79% to 24.85%. Furthermore, speech quality and intelligibility scores experienced marked improvements. The application of speech recognition enhancement and compressed perception technologies emerges as a vital strategy for enriching Russian translation pedagogy and fostering the development of proficient translators.