METHOD FOR BUILDING EMOTIONAL-SPEECH RECOGNITION MODEL BY USING NEURO-FUZZY NETWORK WITH A WEIGHTED FUZZY MEMBERSHIP FUNCTION

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. [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