Confidence measures represent the degree of match between speech data and models, and thus can be utilized to spot errors in voice command systems, improving their reliability. In recent years, systems based on identity vector (i-vector) and Probabilistic Linear Discriminant Analysis (PLDA) have been proven effective in the task of Speaker Verification (SV). This study proposes i-vector and PLDA as a confidence measure for voice command system without the need for acoustic or language models and demonstrates fair performance. Furthermore, in consideration of the deficiency of such i-vectors in modeling temporal information, this study proposes a fusion approach of such system with DTW, enhancing its time sequence discrimination ability.