论文标题
通过改进的交叉缝线网络探索说话者验证的通用语音属性
Exploring Universal Speech Attributes for Speaker Verification with an Improved Cross-stitch Network
论文作者
论文摘要
本文介绍了基于X-Vector的说话者验证（SV）的通用语音属性。表达的方式和地点构成了基本语音属性单元（SAU），然后由绑定的Tri-sau态生成声音建模的新语音属性（NSA）单位。改进的跨缝网络被用作多任务学习（MTL）框架，用于将这些通用语音属性集成到X-Vector网络培训过程中。实验是在核心核心的常见条件5（CC5）和NIST SRE10评估集的10 s-10 s检验上进行的，并且所提出的算法可以在这两个任务上对基线X-Vector进行一致的改进。
The universal speech attributes for x-vector based speaker verification (SV) are addressed in this paper. The manner and place of articulation form the fundamental speech attribute unit (SAU), and then new speech attribute (NSA) units for acoustic modeling are generated by tied tri-SAU states. An improved cross-stitch network is adopted as a multitask learning (MTL) framework for integrating these universal speech attributes into the x-vector network training process. Experiments are conducted on common condition 5 (CC5) of the core-core and the 10 s-10 s tests of the NIST SRE10 evaluation set, and the proposed algorithm can achieve consistent improvements over the baseline x-vector on both these tasks.
