Knowledge Commons of Institute of Automation,CAS
Discriminative Learning for Monaural Speech Separation Using Deep Embedding Features | |
Fan, Cunhang1,3; Liu, Bin1; Tao, Jianhua1,2,3; Yi, Jiangyan1; Wen, Zhengqi1 | |
2019-09 | |
会议名称 | Annual Conference of the International Speech Communication Association |
会议日期 | September 15–19, 2019 |
会议地点 | Graz, Austria |
摘要 | Deep clustering (DC) and utterance-level permutation invariant training (uPIT) have been demonstrated promising for speakerindependent speech separation. DC is usually formulated as two-step processes: embedding learning and embedding clustering, which results in complex separation pipelines and a huge obstacle in directly optimizing the actual separation objectives. As for uPIT, it only minimizes the chosen permutation with the lowest mean square error, doesn’t discriminate it with other permutations. In this paper, we propose a discriminative learning method for speaker-independent speech separation using deep embedding features. Firstly, a DC network is trained to extract deep embedding features, which contain each source’s information and have an advantage in discriminating each target speakers. Then these features are used as the input for uPIT to directly separate the different sources. Finally, uPIT and DC are jointly trained, which directly optimizes the actual separation objectives. Moreover, in order to maximize the distance of each permutation, the discriminative learning is applied to fine tuning the whole model. Our experiments are conducted on WSJ0-2mix dataset. Experimental results show that the proposed models achieve better performances than DC and uPIT for speaker-independent speech separation. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 语音识别与合成 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44384 |
专题 | 多模态人工智能系统全国重点实验室_智能交互 |
通讯作者 | Tao, Jianhua |
作者单位 | 1.NLPR, Institute of Automation, Chinese Academy of Sciences 2.CAS Center for Excellence in Brain Science and Intelligence Technology 3.School of Artificial Intelligence, University of Chinese Academy of Sciences |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Fan, Cunhang,Liu, Bin,Tao, Jianhua,et al. Discriminative Learning for Monaural Speech Separation Using Deep Embedding Features[C],2019. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
interspeech2019.pdf(320KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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