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Vocal Effort Detection Based on Spectral Information Entropy Feature and Model Fusion
Chao, Hao,Lu, Bao-Yun,Liu, Yong-Li,Zhi, Hui-Lai Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.1
Vocal effort detection is important for both robust speech recognition and speaker recognition. In this paper, the spectral information entropy feature which contains more salient information regarding the vocal effort level is firstly proposed. Then, the model fusion method based on complementary model is presented to recognize vocal effort level. Experiments are conducted on isolated words test set, and the results show the spectral information entropy has the best performance among the three kinds of features. Meanwhile, the recognition accuracy of all vocal effort levels reaches 81.6%. Thus, potential of the proposed method is demonstrated.
Vocal Effort Detection Based on Spectral Information Entropy Feature and Model Fusion
( Hao Chao ),( Bao-yun Lu ),( Yong-li Liu ),( Hui-lai Zhi ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.1
Vocal effort detection is important for both robust speech recognition and speaker recognition. In this paper, the spectral information entropy feature which contains more salient information regarding the vocal effort level is firstly proposed. Then, the model fusion method based on complementary model is presented to recognize vocal effort level. Experiments are conducted on isolated words test set, and the results show the spectral information entropy has the best performance among the three kinds of features. Meanwhile, the recognition accuracy of all vocal effort levels reaches 81.6%. Thus, potential of the proposed method is demonstrated.