Oral English pronunciation evaluation system based on PID algorithm

In order to solve the problem of systematic pronunciation bias caused by negative transfer effect of mother tongue in oral English teaching, this study proposed an intelligent calibration system based on ZN tuning PID control algorithm. By constructing multi-modal speech processing architecture, the system integrates dynamic time regulation algorithm and hidden Markov model HMM to realize phoneme boundary detection, and adopts improved PID controller to dynamically compensate pronunciation errors. Hardware design integrates high-precision voice sensing sensor and Harvard architecture digital signal processor (DSP), supporting 16bit fixed-point operation and 100MHz real-time processing capability; At the software level, Viterbi equalization algorithm and Sigmoid function calibration mechanism are introduced to effectively solve the zero-probability event problem caused by sparse corpus data. The experiment builds 13,500 speech samples based on the Spoken Arabic Digit dataset. The experimental results show that the error detection accuracy of the system is improved by 15.6%-25.8% compared with the traditional scheme, and the calibration quality score is improved by 12.3%-23.1%. The comparative study shows that the ZN tuning PID algorithm is significantly superior to the traditional PID and fuzzy PID schemes in the core indexes of comprehensive performance evaluation, overshoot and adjustment time, which verifies the innovative application value of control theory in the field of speech calibration.