Robust GMM Based Gender Classification using Pitch and RASTA-PLP Parameters of Speech

By Yumin Zeng, Zhen-yang Wu, Tiago H. Falk, Wai-Yip Chan. 2006

99 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. Aurélien Bellet
2. Marc Tommasi
3. Emmanuel Vincent
4. Constantine Kotropoulos
5. M. Kotti
6. Brij Mohan Lal Srivastava
7. Shinji Watanabe
8. Mirco Ravanelli

In Signal Processing

1. Marc Tommasi
2. Emmanuel Vincent
3. Aurélien Bellet
4. Constantine Kotropoulos
5. M. Kotti
6. Brij Mohan Lal Srivastava
7. Shinji Watanabe
8. John R. Hershey

Citations in the corpus

• Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion? (2019, background): “…speech processing algorithms can infer not only the spoken contents from the speech signal, but also the speaker’s identity [5], intention [6–9], gender [10,11], emotional state [12–14], pathological condition [15–17], personality [18,19] and cultural [20,21] attributes to a great extent.”
• Gender classification in two Emotional Speech databases (2008, methodology): “[17] is based on Gaussian mixture models (GMMs) of pitch and spectral perceptual linear predictive coefficients and its accuracy ranges from 95% to 98.”