This paper focuses on detection of a single emotion and verification of a specific emotion type in a test utterance. To utilize a probabilistic output of a classifier as well as to exploit various long term acoustic features, we built a probabilistic output SVM and applied several approximated log likelihood ratio tests for emotion verification. Experimental results on SUSAS and AIBO emotion database show that anger and sadness are easier emotions to be detected than boredom and happiness. Results also verify the efficacy of applying log likelihood ratio with respect to neutral emotion as a measure for emotion verification.