A Speech Denoising and Enhancement Method for Language Learning Dialogue System Based on Improved FCN and PSC
With the development of speech recognition technology,dialogue systems have also been widely applied in the field of language learning.To avoid the performance degradation of the dialogue system caused by various noise interferences on speech sig-nals.A study proposes a speech denoising and enhancement method for language learning dialogue systems based on improved fully convolutional neural networks and phase spectrum compensation.Research on improving the compensation factor in phase spectrum compensation using signal-to-noise ratio,and then introducing a dense convolutional network structure into a fully convolutional neu-ral network to improve it.The experimental results show that the designed speech enhancement method has improved the segmented signal-to-noise ratio by 30.54%,23.55%,18.45%,and 9.45%compared to the original noisy speech under different input signal-to-noise ratios.And in different scenarios,their perceived speech quality evaluation scores are all above 2.5.This indicates that the research design method can achieve speech enhancement and denoising with fast computational efficiency,helping students learn Eng-lish speaking.
language learningdialogue systemspeech enhancementspeech denoisingdense convolutional network
万方数据
维普
