Language recognition method based on hierarchical space analysis
一种层次化空间分析方法在语种识别系统中的应用

In automatic spoken language recognition system on telephone conversation speech, differences between train and test utterances on channels, gender and speakers are the key factor of improving the performance of the system. This paper proposed a hierarchical space analysis method. Firstly, it mapped the front-end cepstral features of SDC into the HDLA space, aiming at increasing the discriminability between different languages. Secondly, it selected the characters of adaptive GMM super vector by the method of PCA, which eliminated the influences of different channels, speakers and so on. Experiment results indicate that this method is better for improving the system's performance than the original baseline system.