Phonetically-constrained PLDA modeling for text-dependent speaker verification with multiple short utterances

By Anthony Larcher, Kong Aik LEE, Bin Ma, Haizhou Li. 2013

71 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. Yoshua Bengio
2. Ignacio Lopez-Moreno
3. Patrick Kenny
4. James R. Glass
5. Hung-yi Lee
6. Noam Shazeer
7. Quoc V. Le
8. Jinyu Li

In Signal Processing

1. Patrick Kenny
2. Ignacio Lopez-Moreno
3. Pierre Ouellet
4. Pierre Dumouchel
5. Javier Gonzalez-Dominguez
6. Georg Heigold
7. Erik McDermott
8. Ehsan Variani

Citations in the corpus

• End-to-end text-dependent speaker verification (2015, background): “For example, the combination of i-vector and probabilistic linear discriminant analysis (PLDA) [5, 6] has become the dominant approach, both for text-independent speaker verification [7, 8, 5, 6] and text-dependent speaker verification [9, 10, 11].”
• Deep neural networks for small footprint text-dependent speaker verification (2014, methodology): “The i-vector-PLDA technique and its variants have also been successfully used in text-dependent speaker recognition tasks [8, 9, 10].”
• Text-dependent speaker recognition using PLDA with uncertainty propagation (2013, result): “The current research in the field has shown that conventional approaches are superior to the i-vector-based ones, [5], [6], [7], [8].”