This paper describes syntax/semantics oriented spoken Japanese understanding systems named "SPOJUSSYNO/SEMO" and compares them. At first these systems make Hidden-Markov-Models (HMM) based on word units automatically by concatenating syllables. Then a word lattice is hypothsized by using a word spotting algorithm and word-based HMMs for an input utterance. In SPOJUS-SYNO, the time-synchronous left-to-right parsing algorithm is executed to find the best word sequence from the word lattice according to syntactic & semantic knowledge represented by a context free semantic grammar. In SPOJUS-SEMO, the knowledges of syntax and semantics are represented by a dependency and case grammar. These systems were implemented in the "UNIX-QA" task with the vocabulary size of 521 words. Experimental result shows that the sentence recognition/understanding rate was about 80/87% for six male speakers for the SPOJUS-SYNO, but was very low performance for the SPOJUS-SEMO.