But the problem with HMM's is that they need to be carefully tailored for a specific type of random process. Not only is the segment size chosen specially, but so is the observation space (the feature set). It is difficult for an HMM designed for speech recognition to operate well for other types of processes except speech. If separate HMM's are used, the likelihood values cannot be directly compared in a classifier. The class-specific method solves this problem by allowing two or more HMM's to be used as detectors for their respective model class, while solving the problem of comparing the outputs optimally.