Syllable lattice based Chinese speech retrieval techniques and removing redundancy method from indices

Nowadays, the amount of spoken data becomes much larger on Internet. Thus content based, rapid and accurate speech retrieval techniques are desired. In the research of syllable lattice based Chinese speech retrieval, a retrieval method based on keyword spotting techniques is present, instead of the method based on vector space model. Then, a removing redundancy method is also proposed, which can distinguish useful information from redundant information by a syllable posterior probability histogram and then remove redundancy from lattice indices. Experiment shows that our retrieval method has much better performances than the method based on vector space model. Moreover, smaller indices size and faster searching speed are acquired by using the removing redundancy method.