Abstract: In order to recognize continuous speech in Changsha dialect with a large vocabulary,a gated linear element neural network model based on Connectionist Temporal Classification(CTC) algorithm is proposed.Firstly,the characteristic parameters of speech are extracted by Mel-scale Frequency Cepstral Coefficients(MFCC),and then the extracted characteristic parameters are input into gated linear unit neural network.CTC algorithm is used for training and optimization,and the whole prediction label of input sequence is obtained.Finally,the results of the CTC model,the GRU model and the CTC-GRU model are compared on the self-built corpus of Changsha dialect,and the Word Error Rate(WER) is taken as the evaluation index.The results show that the CTC-GRU model can achieve faster convergence and greater accuracy compared with the other two models.

Key words: CTC-GRU model, MFCC, Changsha dialect recognition, WER