Abstract: Utterance rejection is becoming increasingly important as speech recognition systems continuously migrate from the laboratory to actual applications. Proposed in this paper are state and state duration dependent acoustic confidence measures for acceptance/rejection of recognition hypothesis in speech recognition systems based on hidden Markov model （HMM）. The state dependent confidence measure is computed for each frame of speech as the feature vectors output probability or posteriori state probability given the observation features. It is easy to be implemented by using one single global threshold and no extra training is needed. The state duration dependent one is based on the duration distribution probability and confidence interval theory. Although it is required that the state duration distribution be trained, the data can be easily obtained during the traditional HMM training. Experiment results show that the methods can reject incorrect candidates and OOV （out of vocabulary） words effectively, thus significantly increasing the recognition accuracy with low rejection rate.