Adaptive recognition of different accents conversations based on convolutional neural network

摘要

In this paper, an adaptive recognition of different accents conversations is proposed based on Convolutional Neural Network(CNN), which is used to deal with dialogue speech recognition problems that contain different accents in the CALL_CENTER environment. For the first time, the Mel-Frequency Cepstral Coefficients (MFCC) feature and the SPECTROGRAM feature are combined as the input of CNN to train the speakers' voice feature model and to estimate the change point. Then, an accent classification method based on weighted fusion feature is proposed, and we introduced the IFLY voice recognition system to propose different accent dialogue recognition models based on speaker segmentation. In the experiments, a real database about the dialogue voice related to insurance sales and real estate sales industry is used to be dataset. After a comparative experiment, the results show that the word error rate for speech recognition after speaker segmentation and accent classification was reduced by 20% compared to the original speech recognition word error rate.