In this paper we investigate robust word verification issues in noise using the generalized word posterior probability (GWPP). In computing GWPP, reduced search space, relaxed time registrations of hypothesized words in the word graph, and optimal acoustic and language model weights are employed. The sensitivity of word verification errors with respect to the parameters of GWPP was tested under different SNR conditions. We found that around the optimal parameter settings, there exists a relatively stable region where the total number of word verification errors is fairly insensitive (robust) to the exact choice of the optimal values. Cross-SNR condition tests using a large vocabulary speaker independent, continuous Japanese speech database (Basic Travel Expression Corpus) confirms the robustness of the GWPP based word verification in different SNR's.