Research On Human-Computer Interaction Feedback Models for English Listening Based on Ai Speech Generation and Automatic Scoring in Wireless Network Environments'

This study introduces the idea of a novel HCI feedback system designed to enhance English listening comprehension by utilizing wireless networks, speech synthesis, and automatic scoring. The flexibility component is sometimes overlooked since the traditional methods of conducting a listening assessment are typically subjectivist and quite inflexible. The framework provides adaptive, real-time, learner-specific feedback that can be provided textually, graphically, or auditorily through an advanced TTS model and ASR. Ultra-low latency wireless connection, DNN-based scoring models, and prosody analysis for pitch, rhythm, stress, and fluency evaluation paradigms are all part of the system's design. Improvements in automatic fluency scoring, rhythm stability, and sentence-level stress detection were demonstrated by the experimental findings, which fully justified the pursuit and expansion of the system's design. Through encouraging, involving, and supporting student performance in dynamic learning contexts, this study makes a substantial addition to AI-assisted language instruction.