Speaker recognition from coded speech and the effects of score normalization

By Robert B. Dunn, Thomas F. Quatieri, D. A. Reynolds, J. P. Campbell. 2001

34 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. Jan Skoglund
2. Alejandro Luebs
3. Tomi H. Kinnunen
4. Haizhou Li
5. W. Kleijn
6. Quan Wang
7. Thomas C. Walters
8. Felicia S. C. Lim

In Signal Processing

1. Jan Skoglund
2. Alejandro Luebs
3. Jean-Marc Valin
4. Felicia S. C. Lim
5. Thomas C. Walters
6. Tomi H. Kinnunen
7. Haizhou Li
8. W. Kleijn

Citations in the corpus

• Wavenet Based Low Rate Speech Coding (2017, background): “It is known that EER goes up after coding [34] and in this case, it is expected that the spectral resolution of the low rate coder has restricted speaker identifiability.”
• An overview of text-independent speaker recognition: From features to supervectors (2010, background): “In a similar way, handset-dependent T-norm (“HT-norm”) has been proposed [58].”