Effectiveness analysis of speech visualisation technology applied to English speech teaching

At present, English speech teaching has developed into an intelligent form, and speech recognition function is combined in speech teaching to perform spoken English correction, but the accuracy of speech recognition needs to be improved. In order to improve the effect of speech intelligent recognition in English speech teaching, this paper combines speech recognition technology with visualisation technology to propose a speech recognition visualisation technology, designs and implements ASR algorithm based on Conformer encoder and CTC decoder, and realises VITS speech synthesis model. At the same time, this paper uses knowledge distillation method to obtain a lightweight speech synthesis model, uses MobileNetV3 network to realise the lightweight YOLOv5s model, and combines DeepSORT tracking algorithm and statistical function to realise the target statistical function. According to the comprehensive test results, it can be seen that the model proposed in this paper has high speech recognition accuracy and speed. In addition, it can be seen from the comparative test results that the model proposed in this paper has certain advantages in speech recognition compared with the existing research, and can meet the needs of intelligent English pronunciation teaching.