Dialect Language Recognition Based on Multi-Task Learning

Development of deep learning and neural networks in recent years has led to new solutions to the complicated pattern recognition problems of speech recognition. In order to reinforce the protection of Chinese dialects, to improve the accuracy of dialect language recognition and the diversity of speech signal pre-processing modules for language recognition, this paper proposes a single-task dialect language recognition model, SLNet, on the basis of LSTM and currently the most widely used model in the field of speech recognition. Considering the diversity and complexity of Chinese dialects, on the basis of a multi-task learning parameter sharing mechanism, we use a neural network model to discover the implicit correlation characteristics of different dialects and propose the MTLNet, a dialect recognition model based on multilingual tasking. Further considering the regional characteristics of Chinese dialects, we adopt a multi-task learning model based on hard parameter sharing to construct the ATLNet, a multi-task learning neural network model based on auxiliary tasks. We design several sets of experiments to compare a single-task dialect language recognition model with the MTLNet and ATLNet models proposed in this paper. The results show multi-task methods improve the accuracy of language recognition to 80.2% on average and make up the singularity and weak generalization of the single-task model.