For spoken document retrieval, it is very important to con sider Out-of-Vocabulary (OOV) and mis-recognition of spoken words. Therefore, sub-word unit based recognition and retrieval methods have been proposed. This paper describes a Japanese spoken document retrieval system that is robust for considering OOV words and mis-recognition of sub-units. We used individual syllables as sub-word unit in continuous speech recognition and an n-gram sequence of syllables in a recognized syllable-based lattice. We propose an n-gram indexing/retrieval method with distance in the syllable lattice for attacking OOV, recognition errors, and high speed retrieval. We applied this method to academic lecture presentation database of 44 hours, and 0.58(F-value) of the OOV words were detected in less than 2.5 milliseconds.