Conditional Random Fields (CRFs) are probabilistic models for computing the probability p(Y|X) of a possible output label sequence Y=(y1, y2, . . . , yn) given the input feature sequence X=(x1, x2, . . . , xn). CRFs are widely used in Natural Language Processing (NLP). For example, CRFs may be used in the frontend of TTS (Text To Speech) to implement word segmentation, POS (Part Of Speech) tagging, prosodic break prediction, and the like. CRFs use an N-gram feature (generally bigram) to model the relationship between neighboring output types. The computation complexity grows exponentially as N increases.