| As a complex signal which contains the content and emotion,speech is an effective form of communication and expression.Speech emotion recognition is one kind of information processing technology that can judge the speech emotion state by extracting and analyzing the characteristic parameters of emotional speech,which is of great significance to improve the intelligence level of human computer interaction.Based on the background of speech emotion recognition,three parts of speech emotion recognition systems are introduced,namely,the common speech database,emotional features and identification network.The EEMD algorithm is applied to speech feature extraction,we extract the intrinsic mode function energy feature(IMFE)and marginal spectrum amplitude feature(MSA),and select the IMFE,prosodic features and MFCC for feature level fusion,and propose an adaptive fusion kernel extreme learning machine(KELM)of the decision level fusion method for speech emotion recognition.The research work what has done is as below:(1)The EEMD algorithm is adopted to extract the speech emotion feature by the nonlinear non-stationary signal processing method.The traditional speechfeature extraction methods assume that the signal is a short-time stationary signal.Aiming at the limitation of traditional methods,the marginal spectrum amplitude characteristics are extracted based on the EEMD decomposition of the speech emotion signal,and the KELM is used as recognition network.The experiments were conducted in EMO-DB to identify four emotions(joy,sadness,anger and neutral),and the validity of the MSA was verified compared with the results of prosodic features and MFCC.(2)A feature extraction method based on EEMD algorithm is put forward and applied to speech emotion recognition.Emotional speech signal is decomposed into a group of IMF by EEMD,the Spearman Rank correlation coefficient is used to screen the effective component of IMF,and a new feature of speech emotion named IMF energy(IMFE)is obtained by calculating the energy.The results of simulation in Berlin speech database and comparison with the recognition rate of prosodic features and MFCC show that IMFE can effectively identify emotion and the recognition performance of negative emotion is the best.(3)The feature level data fusion is applied to emotional speech recognition.In order to solve the problem of poor recognition performance of the single emotion feature,the IMFE,prosodic features and MFCC are chosen,and different combinations of these three kinds of features are used as the input of the classifier.The results of simulation in Berlin speech database and comparison with the results of single character show that the feature levelfusion improves the recognition performance in a certain extent and three features are complementary,but because of the simple addition of the feature dimension,the recognition rate of feature fusion is lower than that of the single feature in partial emotion.(4)A new method of speech emotion recognition on the basis of fusion KELM is proposed.In order to solve the problem of poor recognition performance of single feature and single classifier,the decision fusion is applied to speech emotion recognition.Firstly,three kinds of speech emotion features are extracted and the corresponding single classifier is trained respectively.At the same time,the numerical output of KELM is transformed into probability output.Then the adaptive weights of the test set are obtained by the decision strategy.The fusion strategy is made on the basis of probability matrix.Finally,the output value is obtained by linear weighting the output probability of each single classifier.The results of simulation in Berlin speech database show that the fusion KELM achieves the best recognition rate in one single emotion and the whole,it is superior to the single feature,feature fusion and the common decision strategy,so fusion KELM is an effective method for speech emotion recognition. |
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