SEEF-ALDR: A Speaker Embedding Enhancement Framework via Adversarial Learning based Disentangled Representation-Reference-Cited by-同舟云学术

SEEF-ALDR: A Speaker Embedding Enhancement Framework via Adversarial Learning based Disentangled Representation

Published:2020-12-07 Issue: Volume: Page:
ISSN:
Container-title:Annual Computer Security Applications Conference
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Author:

Tai Jianwei¹,Jia Xiaoqi²,Huang Qingjia¹,Zhang Weijuan¹,Du Haichao¹,Zhang Shengzhi³

Affiliation:

1. Key Laboratory of Network Assessment Technology, and Institute of Information Engineering, Chinese Academy of Sciences; Beijing Key Laboratory of Network Security and Protection Technology, and Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences, China

2. Chinese Academy of Sciences

3. Metropolitan College Boston University, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3427228.3427274

Reference72 articles.

1. Gautam Bhattacharya Md Jahangir Alam and Patrick Kenny. 2017. Deep Speaker Embeddings for Short-Duration Speaker Verification.. In Interspeech. 1517–1521. Gautam Bhattacharya Md Jahangir Alam and Patrick Kenny. 2017. Deep Speaker Embeddings for Short-Duration Speaker Verification.. In Interspeech. 1517–1521.

2. Generative Adversarial Speaker Embedding Networks for Domain Robust End-to-end Speaker Verification

3. Weicheng Cai Jinkun Chen and Ming Li. 2018. Analysis of length normalization in end-to-end speaker verification system. arXiv preprint arXiv:1806.03209(2018). Weicheng Cai Jinkun Chen and Ming Li. 2018. Analysis of length normalization in end-to-end speaker verification system. arXiv preprint arXiv:1806.03209(2018).

4. Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System

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