Conventional confidence measures for assessing the reliability of ASR output are typically derived from "low-level" information which is obtained during speech recognition decoding. In contract to these approaches, we propose a novel utterance verification scheme which incorporates confidence measures derived from "high-level" knowledge sources. Specifically, we investigate two measures: in-domain confidence, the degree of match between the input utterance and the application domain of the back-end system, and discourse coherence, the consistency between consecutive utterances in a dialogue session. A joint verification confidence is generated by combining these two measures with an orthodox measure based on GPP (generalized posterior probability). The proposed verification scheme was evaluated on spontaneous dialogue via the ATR speech-to-speech translation system. The two proposed measures were effective in improving verification accuracy.