Comparison of Speaker Recognition Approaches for Real Applications
By Sandro Cumani, Pier Domenico Batzu, Daniele Colibro, Claudio Vair, Pietro Laface, Vasileios Vasilakakis. 2011
98 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
- Daniel Povey
- Sanjeev Khudanpur
- David Snyder
- Daniel Garcia-Romero
- Kris Demuynck
- Jenthe Thienpondt
- Brecht Desplanques
- Shinji Watanabe
In Signal Processing
- Daniel Povey
- Sanjeev Khudanpur
- David Snyder
- Daniel Garcia-Romero
- Kris Demuynck
- Jenthe Thienpondt
- Brecht Desplanques
- Shinji Watanabe
Citations in the corpus
- Deep Neural Network Embeddings for Text-Independent Speaker Verification (2017, methodology): “PLDA scores are normalized using adaptive s-norm [24].”
- ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification (2020, methodology): “extracted from the final fullyconnected layer for all systems. Trial scores are produced using the cosine distance between embeddings. Subsequently, all the scores are normalized using adaptive s-norm [26, 27]. The Table 1: EER and MinDCF performance of all systems on the standard VoxCeleb1 and VoxSR”