Full-covariance UBM and heavy-tailed PLDA in i-vector speaker verification
By Pavel Matejka, Ondrej Glembek, Fabio Castaldo, Md. Jahangir Alam, Oldrich Plchot, Patrick Kenny, Lukáš Burget, Jan Honza Cernocký. 2011
208 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
- Joon Son Chung
- Andrew Zisserman
- Arsha Nagrani
- Sakriani Watiasri Sakti
- Satoshi Nakamura
- Andros Tjandra
- Shinji Watanabe
- Daniel Garcia-Romero
In Signal Processing
- Andrew Zisserman
- Arsha Nagrani
- Joon Son Chung
- Sakriani Watiasri Sakti
- Satoshi Nakamura
- Andros Tjandra
- Daniel Garcia-Romero
- Weicheng Cai
Citations in the corpus
- VoxCeleb2: Deep Speaker Recognition (2018, methodology): “Traditionally, the field of speaker recognition has been dominated by i-vectors [11], classified using techniques such as heavy-tailed PLDA [12] and GaussPLDA [13].”
- Deep Speaker: an End-to-End Neural Speaker Embedding System (2017, methodology): “Traditionally, i-vectors have been used to model inter-speaker variability [3]. i-vector-based speaker recognition models perform clas-sification using cosine similarity between i-vectors or more advanced techniques such as PLDA [20], heavy-tailed PLDA [21], and Gauss-PLDA [5].”
- Machine Speech Chain (2020, methodology): “There are several traditional methods for speaker recognition such as i-vectors [47] and PLDA-based approach [48].”
- Generalized I-vector Representation with Phonetic Tokenizations and Tandem Features for both Text Independent and Text Dependent Speaker Verification (2015, background): “Finally, PLDA modeling and score level fusion is presented in Sec 2.3.”
- Speaker verification and spoken language identification using a generalized i-vector framework with phonetic tokenizations and tandem features (2014, background): “and Neural Network [6, 7] for LID and Probabilistic Linear Discriminant Analysis (PLDA) [8, 9] for SV, respectively).”
- Analysis of i-vector Length Normalization in Speaker Recognition Systems (2011, methodology): “The results presented in [5,8] showed superior performance of the HT-PLDA model over G-PLDA for the telephone conditions of NIST SRE 2010.”