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SPEECH EMOTION RECOGNITION MODEL GENERATING METHOD USING NEURO-FUZZY NETWORK BASED ON WEIGHTED FUZZY MEMBERSHIP FUNCTION
SPEECH EMOTION RECOGNITION MODEL GENERATING METHOD USING NEURO-FUZZY NETWORK BASED ON WEIGHTED FUZZY MEMBERSHIP FUNCTION
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机译:基于加权模糊成员函数的神经模糊网络语音情感识别模型生成方法
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摘要
The present invention relates to a speech emotion recognition model generating method using a neuro-fuzzy network based on a weighted fuzzy membership function and, more particularly, to a configuration comprising the steps of: (1) extracting a feature from a speech signal; (2) calculating a takagi-sugeno defuzzification value by using the extracted feature and a neuro-fuzzy network with a weighted fuzzy membership function (NEWFM); (3) displaying the calculated takagi-sugeno defuzzification value on a two-dimensional emotion space of a quadrant. The speech emotion recognition model generating method using a neuro-fuzzy based on a weighted fuzzy membership function proposed in the present invention extracts a feature from a speech signal, calculates a takagi-sugeno defuzzification value by using the NEWFM, and displays the takagi-sugeno defuzzification value on a two-dimensional emotion space of a quadrant, thereby quickly and accurately classifying emotions from the voice of a person.;COPYRIGHT KIPO 2014;[Reference numerals] (AA) Start;(BB) End;(S100) Extract a feature from a speech signal;(S200) Calculate a takagi-sugeno defuzzification value by using the extracted feature and a neuro-fuzzy network with a weighted fuzzy membership function (NEWFM);(S300) Display the calculated takagi-sugeno defuzzification value on a two-dimensional emotion space of a quadrant
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