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Speech signal carries rich emotional information except semantic information. Five common emotions, namely happiness, anger, boredom, fear and sadness,were discussed and recognized through a proposed framework which combines Principal Component Analysis and Back Propagation neutral network. The candidate parameters were refined from 43 to 11 via PCA to stand for a certain emotional type. Two neural network models, One Class One Network and All Class One Network, were employed and compared. The promising result, ranging from 52%-62%, suggests that the framework is feasible to be used for recognizing emotions in spoken utterance.