Fusing multiple systems into a compact lattice index for chinese spoken term detection

We examine the task of spoken term detection in Chinese spontaneous speech with a lattice-based approach. We first compare lattices generated with different units: word, character, tonal and toneless syllables, and also lattices converted from one unit to another unit. Then we combine lattices from multiple systems into a single lattice. By fully exploiting the redundant information in the combined lattice with a time-based node/arc merging, we achieve the result of a compact lattice index with the accuracy improved to 79.2% from 73.9% using the best subsystem.