1. An apparatus for emotion recognition, the apparatus comprising a processor that comprises: a frame parameter generator configured to detect a plurality of unit frames from an input speech and to generate a parameter vector for each of the unit frames; a key-frame selector configured to select a unit frame as a key frame among the plurality of unit frames; an emotion-probability calculator configured to calculate an emotion probability of the selected key frame; and an emotion determiner configured to determine an emotion of a speaker based on the calculated emotion probability, wherein the key-frame selector is configured to select a unit frame with a lower probability of presence than a predetermined fraction of the plurality of unit frames as the key frame, and wherein the emotion-probability calculator is configured to calculate the emotion probability by extracting a global feature from the selected key frame and classifying an emotion of the speaker into at least one of predefined emotion categories using a support vector machine (SVM) mechanism and the global feature, or by classifying an emotion of the speaker into at least one emotion category that corresponds to a generative model that is capable of generating a largest number of parameter vectors same as or similar to those of the key frames, wherein the generative model is one of Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM), which are obtained from learning each emotion category.