Self-Study System Assessment of Spoken English considering the Speech Scientific Computing Knowledge Assessment Algorithm

For the purpose of effectively improving the assessment accuracy of spoken English self-study quality of English learners, a speech scientific computing knowledge assessment algorithm is introduced into the assessment of the self-study system of spoken English. Through the combination of spoken English speech assessment and accuracy detection, a DSP-based self-study assessment system for spoken English is designed. This system is mainly divided into two parts: voice signal processing and hardware circuit design. The features of spoken English self-study speech are extracted based on the multilayer wavelet feature conversion method, and the English pronunciation is detected and analyzed by adaptive filtering based on the characteristic values obtained. The corresponding wavelet entropy features of spoken English self-study speech are automatically assessed and assessed for the self-study quality. Finally, a practical case is analyzed, and the results indicate that the system proposed in this paper has high accuracy and excellent stability in assessing the quality of self-study in spoken English. Hence, it is of practical value in self-study assessment of spoken English.