10. A method for emotion recognition, the method comprising: detecting a plurality of unit frames from an input speech and generating a parameter vector for each of the unit frames; selecting a unit frame as a key frame among the plurality of unit frames; calculating an emotion probability for the selected key frame; and using a processor to determine an emotion of a speaker based on the calculated emotion probability, wherein the selecting of the key frame comprises selecting 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 calculating of the emotion probability comprises extracting a global feature from the selected key frames 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.
10. A method for emotion recognition, the method comprising: detecting a plurality of unit frames from an input speech and generating a parameter vector for each of the unit frames; selecting a unit frame as a key frame among the plurality of unit frames; calculating an emotion probability for the selected key frame; and using a processor to determine an emotion of a speaker based on the calculated emotion probability, wherein the selecting of the key frame comprises selecting 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 calculating of the emotion probability comprises extracting a global feature from the selected key frames 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. 14. The method of claim 10 , wherein the selecting of the key frame comprises: calculating an occurrence probability of each unit frame occurring within the plurality of unit frames; calculating a presence probability of each unit frame present within a plurality of previously stored reference frames; assigning a first relevance value to each unit frame with a higher occurrence probability, and assigning a second relevance value to the each unit frame with a higher presence probability, wherein the first relevance value indicates a higher probability of being selected as a key frame and the second relevance value indicates a lower probability of being selected as a key frame, and estimating relevance of each unit frame by taking into consideration both the first relevance value and the second relevance value; and determining the unit frame as the key frame according to the assigned first and second relevance values.