Quality evaluation of English pronunciation based on artificial emotion recognition and gaussian mixture model

Abstract

At present, the posterior probability measure widely used in English speech recognition has the situation that the posterior probability measure of different phonemes cannot be consistent to measure the pronunciation quality of the phoneme and the acoustic modeling method of voice recognition is inconsistent with the evaluation target. Therefore, in order to improve the evaluation effect of English pronunciation quality in colleges and universities, this article is based on artificial emotion recognition and high-speed hybrid model to analyze and filter various clutters that affect speech quality to improve students’ English speech recognition. Moreover, this article uses the characteristics of the clutter and the target in the data to conform to different distributions and based on the clutter distribution characteristics obtained by statistics, this article realizes the suppression of the clutter to improve the target detection performance. In addition, the method proposed in this paper solves the limitations of the clutter suppression technology in the traditional voice detection system and improves the target detection performance. In order to study the pronunciation quality evaluation effect of this model and its effect in English teaching, this paper designs a controlled experiment to analyze the model’s performance. The research results show that the model constructed in this paper has good performance.