An English vocabulary pronunciation evaluation model based on multidimensional audio features and machine learning

In response to the issue where current English vocabulary pronunciation evaluation models cannot fully extract feature information from different dimensions of spectrograms, this paper first designs a multi-dimensional audio feature extraction algorithm based on multi-scale dilated convolution.This algorithm initially constructs a shallow feature refinement module that uses parallel convolutions to capture time, frequency, and time-frequency three-dimensional shallow features of Mel-frequency cepstral coefficients features.It combines Res2net structure, dilated convolution, and channel attention to capture more fine-grained multi-scale information from the shallow multi-dimensional features.Then it employs a global feature fusion module combined with multiplicative gating mechanisms to enhance cross-scale feature fusion.Finally, differential evolution algorithm optimised support vector machines are used to score the multi-dimensional features.Experimental results indicate that the average evaluation accuracy of the proposed model reaches 94.57%, outperforming comparative models and achieving an objective and accurate assessment of English vocabulary pronunciation.