A joint factor analysis approach to whispering speaker identification under mismatched speaking manners and channels

摘要

In order to increase the recognition accuracy under different speaking manners and different channels, the paper introduces joint factor analysis into the recognition system by training speaker and channel space. Then based on analyzing distribution of the channel factor by principal component analysis method, vector quantization algorithm is proposed by the paper to achieve the optimal channel space matrix which avoids the instability of channel factor caused from the randomness of initial channel space. The experiment result shows that the modified joint factor analysis system can increase the recognition accuracy compared to traditional joint factor analysis method under complex conditions.