Wangd-wiki-article-2020-nb

2017

Back to 2017, we set our goal of deep speech factorizatoin. The first paper is published on ICASSP 2018

Lantian Li, Dong Wang, Yixiang Chen, Ying Shi, Zhiyuan Tang, "DEEP FACTORIZATION FOR SPEECH SIGNAL", ICASSP 2018. [1]

We noticed the problem of soft-max based training, due to the discardxing of the output layers

Lantian Li, Zhiyuan Tang, Dong Wang, "FULL-INFO TRAINING FOR DEEP SPEAKER FEATURE LEARNING", ICASSP 2018. [2]

2018/12/26, propose the idea of deep statistical speaker representation. That was based on VAE [3]

We noticed the impact of irregulation of deep speaker vectors, and tried to present normalization approaches

Yang Zhang and Lantian Li and Dong Wang, VAE-based regularization for deep speaker embedding, Interspeech 2019. [4]

2019/04/20, "Normalization in speaker embedding", Speaker recognition workshop, Kunshan, Shanghai, [5]
2019/07/17, Deep Feature Learning and Normalization for Speaker Recognition, report in India summr school [6]
2019/08/14, present the first proposal that uses flow to model deep speaker featrues. (Report in Huawei group discussion)
2019/10/27, present the initial idea of using flow to perform factorization, CSLT weekly meeting [7]

2019/11/12, CYQ start to work on DNF, using the subspace of the dimension to discriminte speakers [cvss 714]
2019/12/20, I start to work on NF with constraint training. More understanding acheived for LDA. [cvss 741]
2020/1/23, I noticed a bug in the DNF code, where the residual space was infact trained, so it is not a true dim-split DNF we hoped. [cvss 741]
2020/1/27, I conjectured the normalization role of DNF, and informed YQ to perform a full-space experiment. The results are good. [8] [9]
2020/1/28, I confirmed the normalization role of LDA for x-vectors. This forms the basic argument for the deep norm paper. [cvss 741]
2020/02/10, Dong Wang, Deep Generative Models for Discriminative Tasks, CSLT weekly meeting. Present DNF