关键词: 说话人识别, 深度学习, 特征表达, 模式分类, 端到端

Abstract:

Because of its unique convenience, economy and accuracy, speaker recognition has become an important security certification technology in people’s daily life and work. However, the complex environment in practical applications poses a huge challenge to the robustness of speaker recognition system. In recent years, deep learning has performed well in feature expression and pattern classification, providing a new direction for the further development of speaker recognition technology. Compared with the traditional speaker recognition technologies（such as GMM-UBM, GMM-SVM, JFA, i-vector, etc.）, this paper focuses on speaker recognition methods under the framework of deep learning. Depending on the role of deep learning in speaker recognition, these research methods are divided into three categories：feature expression based on deep learning, back-end modeling based on deep learning, and end-to-end joint optimization. The characteristics of typical algorithms and network structure are dissected and summarized, and their specific performances are compared and analyzed. Finally, it summarizes the application properties and advantages of deep learning in speaker recognition, and analyzes the problems and challenges currently faced in speaker recognition research. The prospects of speaker recognition research under the framework of deep learning are also looked forward to promote the further development of speaker recognition technology.

Key words: speaker recognition, deep learning, feature expression, pattern classification, end to end