A study of lattice-based spoken term detection for Chinese spontaneous speech

We examine the task of spoken term detection in Chinese spontaneous speech with a lattice-based approach. We compare lattices generated with different units: word, character, tonal syllable and toneless syllable, and also look into methods of converting lattices from one unit to another one. We find the best system is with toneless-syllable lattices converted from word lattices. Further improvement is achieved by lattice post-processing and system combination. Our best system has an accuracy of 80.2% on a keyword spotting task.