目前，在英语语音识别中广泛使用的后验概率度量存在以下情况：不同音素的后验概率度量不能一致地衡量音素的发音质量，并且语音识别的声学建模方法与评估目标不一致。因此，为了提高高校英语语音质量的评价效果，本文基于人工情感识别和高速混合模型，对影响语音质量的各种杂波进行分析和过滤，以提高学生的英语语音识别能力。此外，本文根据统计数据获得的杂波分布特征，利用数据中杂波和目标的特征来适应不同的分布，本文实现了杂波抑制，以提高目标检测性能。另外，本文提出的方法解决了传统语音检测系统中杂波抑制技术的局限性，提高了目标检测性能。为了研究该模型的语音质量评价效果及其在英语教学中的效果，本文设计了一个对照实验来分析该模型的性能。研究结果表明，本文构建的模型具有良好的性能。本文提出的方法解决了传统语音检测系统中杂波抑制技术的局限性，提高了目标检测性能。为了研究该模型的语音质量评价效果及其在英语教学中的效果，本文设计了一个对照实验来分析该模型的性能。研究结果表明，本文构建的模型具有良好的性能。本文提出的方法解决了传统语音检测系统中杂波抑制技术的局限性，提高了目标检测性能。为了研究该模型的语音质量评价效果及其在英语教学中的效果，本文设计了一个对照实验来分析该模型的性能。研究结果表明，本文构建的模型具有良好的性能。
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.
