ISSN 1000-1239 CN 11-1777/TP

• 人工智能 •

### 基于多任务学习的方言语种识别

1. 1(西北大学信息科学与技术学院 西安 710127);2(陕西师范大学计算机学院 西安 710119) (qcgnwu@stumail.nwu.edu.cn)
• 出版日期: 2019-12-01
• 基金资助:
国家自然科学基金项目(61572401，61701400)；中央高校基本科研业务费专项资金项目(GK201803063)；陕西省自然科学基础研究计划项目(2019JQ-271)

### Dialect Language Recognition Based on Multi-Task Learning

Qin Chenguang1, Wang Hai1, Ren Jie2, Zheng Jie1, Yuan Lu1, Zhao Zixin1

1. 1(School of Information Science & Technology, Northwest University, Xi’an 710127);2(School of Computer Science, Shaanxi Normal University, Xi’an 710119)
• Online: 2019-12-01

Abstract: 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.