Robust Children and Adults Speech Classification

A robust classification system for children, adults male and adults female speech has been proposed. The proposed classifier is based on Gaussian Mixture Models, which apply the combined parameters of pitch, first three formants and 5-order relative spectral perceptual linear predictive coefficients (RASTA-PLPC) and Delta RASTA-PLPC, to model the characteristics of children speech, adult male and female speech. Its performances have been evaluated for several speech databases. The simulation results show that: for the speeches of adults and children in grades K to 5 (below the age of 11), the total accuracy of classification is above 97% in the condition of clean speech and still remains nearly 90% even if the SNR of speech is degraded to 5 dB in the condition of different additive noises and channel distortion noises. Furthermore, the performance of this classifier is independent of languages.