The invention provides a network teaching method and system with a voice assessment function. According to the voice assessment method provided by the invention, a phoneme state of a voice is used for replacing a multi-Gaussian mixture model trained by a conventional Mel-frequency cepstral coefficient (MFCC), and a posterior probability and a zero-order Baum-Welch statistical magnitude are calculated according to the feature. A voice feature based on phonemes is extracted through a multi-language phoneme identifier. A feature based on multi-language extraction is complementary during catching of non-native pronunciation information, and a feature based on phoneme duration is effective in automatic native accent assessment. Finally, a fusion system is provided in the method, so that Spearman relevant coefficients of 0.5706 and 0.6089 are reached on a development set and a test set. As indicated by the relevant coefficients, the method provided by the invention is very accurate and effective in oral speech assessment.