Speech emotion recognition based on sparse representation

• Autorzy: Yan J. , Wang X. , Gu W. , Ma L.
• Języki publikacji: EN

Abstrakty

EN

Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

Słowa kluczowe

EN

speech emotion recognition; sparse partial least squares regression SPLSR; SPLSR; feature selection and dimensionality reduction

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2013
• Tom: Vol. 38, No. 4
• Strony: 465--470
• Opis fizyczny: Bibliogr. 39 poz., rys., tab.

Twórcy

autor

Yan J.

• The School of Information Science and Engineering, Southeast University No.2 Sipailou, Nanjing, P.R.China

autor

Wang X.

• Research Center for Learning Science, Southeast University No.2 Sipailou, Nanjing, P.R.China

autor

Gu W.

• Research Center for Learning Science, Southeast University No.2 Sipailou, Nanjing, P.R.China

autor

Ma L.

• Research Center for Learning Science, Southeast University No.2 Sipailou, Nanjing, P.R.China

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