Due to the difference of the speaker's language, speech emotion recognition tasks often face the situation that training data are not fully representative of test data. Therefore, the space extended by a kernel function. might not sufficient to describe different properties of data and thus produce a satisfactory decision function. In this wok, we apply multiple kernel learning to recognize the speech emotion of cross-language. Compared to SVM, multiple kernel learning can achieve better performance in cross-language speech emotion recognition tasks.
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Bach Francis R, 2004, ICML, DOI 10.1145/1015330.1015424
机构:
School of Information Technology, Indian Institute of Technology Kharagpur, KharagpurSchool of Information Technology, Indian Institute of Technology Kharagpur, Kharagpur
Rao K.S.
Koolagudi S.G.
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School of Information Technology, Indian Institute of Technology Kharagpur, KharagpurSchool of Information Technology, Indian Institute of Technology Kharagpur, Kharagpur
Koolagudi S.G.
Vempada R.R.
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School of Information Technology, Indian Institute of Technology Kharagpur, KharagpurSchool of Information Technology, Indian Institute of Technology Kharagpur, Kharagpur
机构:
School of Information Technology, Indian Institute of Technology Kharagpur, KharagpurSchool of Information Technology, Indian Institute of Technology Kharagpur, Kharagpur
Rao K.S.
Koolagudi S.G.
论文数: 0引用数: 0
h-index: 0
机构:
School of Information Technology, Indian Institute of Technology Kharagpur, KharagpurSchool of Information Technology, Indian Institute of Technology Kharagpur, Kharagpur
Koolagudi S.G.
Vempada R.R.
论文数: 0引用数: 0
h-index: 0
机构:
School of Information Technology, Indian Institute of Technology Kharagpur, KharagpurSchool of Information Technology, Indian Institute of Technology Kharagpur, Kharagpur