Generalized I-vector Representation with Phonetic Tokenizations and Tandem Features for both Text Independent and Text Dependent Speaker Verification

By Ming Li, Lun Liu, Weicheng Cai, Wenbo Liu. 2015

21 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Weicheng Cai
2. Jinkun Chen
3. Ming Li
4. Zexin Cai
5. Xiaoqi Wang
6. Xiangjinzi Zhang
7. Joon Son Chung
8. Andrew Zisserman

In Signal Processing

1. Weicheng Cai
2. Jinkun Chen
3. Ming Li
4. Xiaoqi Wang
5. Xiangjinzi Zhang
6. Zexin Cai

Citations in the corpus

• Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System (2018, background): “…perceptual linear prediction (PLP), shifted delta coeffi-cients (SDC) features [2, 5], and automatically learned phoneme discriminant features from deep neural networks (DNN), such as bottleneck features [6, 7, 8], phoneme posterior probability (PPP) features [9], and tandem features [10, 11].”
• A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification (2018, methodology): “Once we have a trained tokenizer, usually a bag-of-words (BoW) or N-gram model is used to form the encoded representation [11, 12].”