Knowledge Commons of Institute of Automation,CAS
Exploring wav2vec 2.0 on speaker verification and language identification | |
Fan ZY(范志赟)1,2; Li M(李蒙)1; Zhou SY(周世玉)1; Xu B(徐波)1 | |
2021-09 | |
会议名称 | INTERSPEECH2021 |
会议日期 | 2021-8-30 |
会议地点 | 线上会议 |
摘要 | Wav2vec 2.0 is a recently proposed self-supervised framework for speech representation learning. It follows a two-stage training process of pre-training and fine-tuning, and performs well in speech recognition tasks especially ultra-low resource cases. In this work, we attempt to extend the self-supervised framework to speaker verification and language identification. First, we use some preliminary experiments to indicate that wav2vec 2.0 can capture the information about the speaker and language. Then we demonstrate the effectiveness of wav2vec 2.0 on the two tasks respectively. For speaker verification, we obtain a competitive result with the Equal Error Rate (EER) of 3.61% on the VoxCeleb1 dataset. For language identification, we obtain an EER of 12.02% on the 1 second condition and an EER of 3.47% on the full-length condition of the AP17-OLR dataset. Finally, we utilize one model to achieve the unified modeling by the multi-task learning for the two tasks. |
关键词 | self-supervised speaker verification language identification multi-task learning wav2vec 2.0 |
学科门类 | 工学 |
收录类别 | EI |
七大方向——子方向分类 | 语音识别与合成 |
国重实验室规划方向分类 | 语音语言处理 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49730 |
专题 | 复杂系统认知与决策实验室_听觉模型与认知计算 |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Fan ZY,Li M,Zhou SY,et al. Exploring wav2vec 2.0 on speaker verification and language identification[C],2021. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Exploring wav2vec 2.(2081KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论