Robust GMM Based Gender Classification using Pitch and RASTA-PLP Parameters of Speech
Yumin Zeng, Zhenyang Wu, Tiago Henrique Falk, Wai-Yip Chan · 2006
A novel gender classification system has been proposed based on Gaussian mixture models, which apply the combined parameters of pitch and 10thorder relative spectral perceptual linear predictive coefficients to model the characteristics of male and female speech. The performances of gender classification system have been evaluated on the conditions of clean speech, noisy speech and multi-language. The simulations show that the performance of the proposed gender classifier is excellent; it is very robust for noise and completely independent of languages; the classification accuracy is as high as above 98% for all clean speech and remains 95% for most noisy speech, even the SNR of speech is degraded to OdB