Combining evidence from residual phase and MFCC features for speaker recognition

By K. Sri Rama Murty, Bayya Yegnanarayana. IEEE Signal Processing Letters, 2006

465 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/83ca1ac19e12a19179dcde4867a90c3a690acce0

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. Zhen Huang
2. Chin-Hui Lee
3. Lirong Dai
4. K. Sri Rama Murty
5. Jun Du
6. Bayya Yegnanarayana
7. Yong Xu
8. Jinyu Li

In Signal Processing

1. Chin-Hui Lee
2. Zhen Huang
3. Jun Du
4. Lirong Dai
5. Yong Xu
6. K. Sri Rama Murty
7. Bayya Yegnanarayana
8. Deliang Wang

Citations in the corpus

• Multi-objective learning and mask-based post-processing for deep neural network based speech enhancement (2017, background): “MFCC is one of the most popular speech features used in speech recognition [29], speaker recognition [30] and music modeling [ ?”
• Epoch Extraction From Speech Signals (2008, background): “It has been shown that the excitation features derived from the regions around the epoch locations provide complementary speaker-specific information to the existing spectral features [7], [8].”