To achieve efficient a d compact low-dimensional features for speech emotion recognition,a novel featurereduction method using uncertain linear discriminant analysis is proposed.Using the same principles as for conven...To achieve efficient a d compact low-dimensional features for speech emotion recognition,a novel featurereduction method using uncertain linear discriminant analysis is proposed.Using the same principles as for conventional linear discriminant analysis(LDA),uncertainties of the noisy or distorted input data ae employed in order to estimate maximaiy discriminant directions.The effectiveness of the proposed uncertain LDA(ULDA)is demonstrated in the Uyghur speech emotion recognition task.The emotional features of Uyghur speech,especially,the fundamental fequency and formant,a e analyzed in the collected emotional data.Then,ULDA is employed in dimensionality reduction of emotional features and better performance is achieved compared with other dimensionality reduction techniques.The speech emotion recognition of Uyghur is implemented by feeding the low-dimensional data to support vector machine(SVM)based on the proposed ULDA.The experimental results show that when employing a appropriate uncertainty estimation algorithm,uncertain LDA outperforms the conveetional LDA counterpart on Uyghur speech emotion recognition.展开更多
基金The National Natural Science Foundation of China(No.61673108,61231002)
文摘To achieve efficient a d compact low-dimensional features for speech emotion recognition,a novel featurereduction method using uncertain linear discriminant analysis is proposed.Using the same principles as for conventional linear discriminant analysis(LDA),uncertainties of the noisy or distorted input data ae employed in order to estimate maximaiy discriminant directions.The effectiveness of the proposed uncertain LDA(ULDA)is demonstrated in the Uyghur speech emotion recognition task.The emotional features of Uyghur speech,especially,the fundamental fequency and formant,a e analyzed in the collected emotional data.Then,ULDA is employed in dimensionality reduction of emotional features and better performance is achieved compared with other dimensionality reduction techniques.The speech emotion recognition of Uyghur is implemented by feeding the low-dimensional data to support vector machine(SVM)based on the proposed ULDA.The experimental results show that when employing a appropriate uncertainty estimation algorithm,uncertain LDA outperforms the conveetional LDA counterpart on Uyghur speech emotion recognition.
