Application of latent semantic analysis in continuous speech recognition
Xiaodong Shi · Computer Engineering and Applications Journal · 2009
The theory of Latent Semantic Analysis(LSA) for speech recognition is described,and the related techniques for implementing LSA-based language modeling in speech recognition systems are presented.An LSA-based semantic model is constructed on the WSJ0 text corpus.This paper uses the interpolation method to combine this semantic model with conventional 3-gram to form a hybrid language mode(li.e.,LSA+3-gram).To optimize the performance of the hybrid model,it applies k-means algorithm to perform vector clustering in the LSA vector space while the density function is used to initialize the centroid.The constructed hybrid language model outperforms the corresponding 3-gram baseline:Continuous speech recognition experiments conducted on the WSJ0 test corpus show a relative reduction in word error rate of about 13.3%.